Ecological interactions in paddy fields: Population dynamics of natural enemies rove beetle in relation to weather parameters

The present study investigates the population dynamics of rove beetle (Paederus fuscipes) in paddy fields across different locations during the Kharif seasons of 2022 and 2023. The study was conducted in Palpur, SHUATS, and Korsand to analyze the seasonal abundance of rove beetles and their correlation with weather parameters. Observations revealed that the rove beetle population first appeared in the 37th Standard Meteorological Week (SMW) and persisted until the 44th SMW. The beetle population, recorded in terms of beetles per hill and percentage beetle population, varied across locations and years. In Palpur, the population ranged from 0.56 (0.14%) to 0.97 (0.24%) in 2023 and from 0.60 (0.15%) to 1.07 (0.27%) in 2022, peaking in the 42nd SMW. Similar trends were recorded in SHUATS and Korsand, where the beetle population peaked during the third week of October before declining in early November. Weather parameters such as temperature, relative humidity, rainfall, wind velocity, and sunshine hours were analyzed for their influence on beetle populations. The pooled data from 2022 and 2023 indicated that the population increased with rising temperatures and sunshine hours while showing a decline with increasing rainfall and wind velocity. Correlation analysis revealed that rove beetle populations were positively correlated with evening relative humidity (r=0.515) and mean relative humidity (r=0.438), while negative correlations were observed with maximum temperature, mean temperature, rainfall, and wind velocity. The findings suggest that weather conditions play a crucial role in determining the population dynamics of rove beetles, influencing their role as natural enemies in rice ecosystems. Understanding these ecological interactions can contribute to the development of sustainable pest management strategies by enhancing the effectiveness of natural predators like rove beetles in controlling paddy insect pests.

Illustrations explain the paper; they do not show study data.

AI-generated illustration

↗ Enlarge

Weather Influence on Rove BeetlesClose ×

AI-generated conceptual illustration (rove beetles in a paddy field). It explains the idea and does not show study data.

Open full-size image ↗

Introduction

Rice (Oryza sativa L.) is one of the most important staple food crops, supporting the livelihood and food security of millions of people worldwide, particularly in Asia, where approximately 90% of global rice is grown and consumed (Pathak et al., 2002) [10] . The productivity of rice is significantly affected by various biotic and abiotic factors, with insect pests posing a major challenge. Over 1400 insect species have been reported to attack rice at different growth stages (Grist and Lever, 1969) [5] , and their management is crucial for sustainable rice production. Climate change, along with agronomic interventions such as crop diversity, irrigation, fertilization, and pesticide application, has significantly influenced the intensity and severity of pest infestations and their natural enemies in rice ecosystems (Way and Heong, 1994; Ooi and Shephard, 1994) [15,9] . Conventional pest management strategies rely heavily on chemical insecticides, leading to environmental concerns, pest resistance, and adverse effects on non-target beneficial organisms, necessitating the adoption of ecologically sustainable alternatives (Gurr et al., 2016) [6] . Natural enemies, including predatory beetles and spiders, play a critical role in regulating pest populations in rice fields. Among these, rove beetles (Paederus fuscipes) and various spider species have gained attention for their potential as biological control agents (Meena et al., 2019) [8] . Their population dynamics are largely influenced by abiotic factors such as temperature, humidity, and rainfall (Dhurva and Soni, 2020) [4] . However, despite their significance in integrated pest management (IPM), there is a lack of comprehensive studies documenting the seasonal incidence and environmental factors affecting these beneficial arthropods. Previous research has primarily focused on pest attraction to light traps, with limited data available on the light trap collection of natural enemies such as predatory beetles and spiders (Atwal et al., 1969; De Bach, 1974; Sharma et al., 2010) [1,3,13] . Given their importance in sustainable agriculture, understanding the population trends of these predators and their correlation with weather parameters is crucial for improving biological pest control measures. Light traps have long been used as an indigenous and eco-friendly pest monitoring tool in rice ecosystems, capturing a diverse range of phototactic insects, including pest species and their natural enemies (Hufnagel et al., 2008) [7] . The efficiency of light traps in assessing insect biodiversity has been demonstrated in various studies, though their role in monitoring the population dynamics of predatory arthropods remains underexplored. Limited research has examined the diversity and seasonal abundance of natural enemies in rice fields, particularly in high-altitude rainfed ecosystems, where insect populations are influenced by unique climatic conditions (Bhattacharyya et al., 2006; Pathak et al., 2014) [11] . Therefore, there is a pressing need to document the diversity and seasonal trends of insect natural enemies to enhance conservation efforts and optimize their use in pest management strategies. The present study aims to analyze the population dynamics of natural enemies, particularly rove beetles and spiders, in rice agroecosystems and assess their relationship with key abiotic factors such as temperature, humidity, rainfall, and sunshine hours. By utilizing light traps and field surveys, this research seeks to provide a comprehensive understanding of the seasonal incidence of these beneficial predators and their potential in sustainable rice pest management. The findings will contribute to biodiversity conservation and integrated pest management efforts, ensuring a balanced approach to pest control while minimizing environmental risks.

Materials and Methods

This study aimed to assess the population dynamics of major insect pests, with a focus on rove beetles and spiders, in relation to climatic factors affecting rice ecosystems. The research was conducted during the Kharif seasons of 2022 and 2023 at three locations: Palpur, SHUATS, Central Research Farm, and Korsand. At each location, a bulk plot of 100 m² of paddy was established to monitor the seasonal build-up of insect pest populations. To assess pest populations, experimented on randomly selected ten hills from adjacent paddy plots surrounding the main trial field each week. They counted the insect pests on these hills and calculated the average population per ten hills to determine seasonal incidence. Rove beetles and other insect pests were regularly monitored to understand their population dynamics and biological control potential. In addition to pest population assessment, observation recorded insect pest damage to the rice crop weekly. They correlated this data with meteorological parameters, including maximum and minimum temperatures, mean temperature, relative humidity, sunshine hours, wind speed, and rainfall. Meteorological data were collected from the Department of Meteorology's observatory at SHUATS, Prayagraj. The correlation between weather parameters and the occurrence, population dynamics, and damage caused by both pest species and beneficial insects, such as rove beetles, was analyzed to determine the impact of climatic factors on pest management in rice.

Results and Discussion Population Dynamics of Rove Beetle (Paederus fuscipes) in Kharif Season Palpur, Tehsil: Karchhana (Kharif 2022 & 2023)

The rove beetle population first appeared in the 37 th (SMW) and persisted until the 44 th SMW in both years. During Kharif 2023, beetle density ranged from 0.56 (0.14%) to 0.97 (0.24%), peaking in the third week of October (42 nd SMW) before declining in early November (44 th SMW). Weather parameters during this period varied: temperature (25.89°C to 35.46°C), relative humidity (54.29% to 85.14%), rainfall (1.57 mm/day), wind velocity (1.25 km/hr), and sunshine hours (7.34 hr/day). In Kharif 2022, beetle density varied between 0.60 (0.15%) and 1.07 (0.27%), also peaking in the third week of October. The weather ranged as follows: temperature (21.20°C to 32.09°C), relative humidity (58.00% to 92.43%), rainfall (0.00 mm/day), wind velocity (1.03 km/hr), and sunshine hours (7.49 hr/day). Correlation analysis revealed a positive association with evening relative humidity (H min = 0.621) and mean relative humidity (H mean = 0.558) in 2022, while in 2023, positive correlations were observed with temperature and sunshine hours but negative with relative humidity, rainfall, and wind velocity. Pooled data analysis confirmed similar trends (Table 1). SHUATS, Naini, Tehsil: Salempur (Kharif 2022 & 2023). In 2023, the rove beetle population ranged from 0.40 (0.10%) to 0.83 (0.21%), peaking during the 42 nd SMW, with a decline from the 44 th SMW. Weather conditions included temperatures between 25.89°C and 35.46°C, relative humidity from 54.29% to 85.14%, rainfall at 1.57 mm/day, wind velocity of 1.25 km/hr, and 7.34 sunshine hours per day. For Kharif 2022, population density ranged from 0.65 (0.16%) to 0.88 (0.22%), peaking in mid-October. Weather parameters were: temperature (21.20°C to 32.09°C), relative humidity (58.00% to 92.43%), rainfall (0.00 mm/day), wind velocity (1.03 km/hr), and sunshine hours (7.49 hr/day). Correlation analysis for 2022 indicated a strong positive relationship with relative humidity and sunshine hours, while in 2023, temperature showed a stronger positive correlation. Pooled data reaffirmed the significant influence of evening relative humidity (H min = 0.522) and sunshine hours on beetle population (Table 2). Korsand, Tehsil: Soraon (Kharif 2022 & 2023). In 2023, the beetle population varied from 0.72 (0.18%) to 1.17 (0.29%), reaching its peak in the 42 nd SMW. Weather conditions during this period included temperatures of 25.89°C to 35.46°C, relative humidity of 54.29% to 85.14%, rainfall of 1.57 mm/day, wind velocity of 1.25 km/hr, and sunshine hours of 7.34 hr/day. During Kharif 2022, the population ranged from 0.76 (0.19%) to 1.25 (0.31%), peaking in mid-October. The weather conditions recorded were temperature (21.20°C to 32.09°C), relative humidity (58.00% to 92.43%), no recorded rainfall, wind velocity of 1.03 km/hr, and 7.49 sunshine hours per day. Correlation analysis highlighted a strong positive correlation with relative humidity in 2022, whereas in 2023, temperature and sunshine hours showed stronger associations. The pooled data validated these findings, with evening relative humidity (H min = 0.495) and sunshine hours significantly influencing the population dynamics (Table 3).

2022	—	—	—	—	—	—	—	—
Parameter	B. /Hill	Mean	Std. D	Std. Error	% P	Mean	Std. D	Std. Error
Bugs/Hill	1.00	0.31	0.47	0.85	1.00	0.08	0.12	0.42
T Max	-0.372NS	34.30	3.55	0.61	-0.372NS	34.30	3.55	0.61
T Min	-0.050NS	23.39	4.88	1.01	-0.050NS	23.39	4.88	1.01
T Mean	-0.194NS	28.84	4.03	0.75	-0.194NS	28.84	4.03	0.75
% H Morning	0.399*	87.90	6.84	0.73	0.400*	87.90	6.84	0.73
% H Evening	0.609**	53.42	10.32	1.41	0.609**	53.42	10.32	1.41
H Mean	0.553**	70.66	8.15	0.97	0.553**	70.66	8.15	0.97
Rainfall	0.098NS	3.75	6.45	3.33	0.099NS	3.75	6.45	3.33
Wind V.	-0.248NS	1.23	0.30	0.27	-0.248NS	1.23	0.30	0.27
Sunshine	0.256NS	5.78	1.70	0.71	0.256NS	5.78	1.70	0.71
2023
Bugs/Hill	1.00	0.29	0.45	0.83	1.00	0.07	0.11	0.41
T Max	0.113NS	34.02	2.62	0.45	0.113NS	34.02	2.62	0.45
T Min	0.173NS	24.11	4.54	0.92	0.173NS	24.11	4.54	0.92
T Mean	0.155NS	29.07	3.49	0.65	0.155NS	29.07	3.49	0.65
% H Morning	-0.193NS	88.26	3.19	0.34	-0.193NS	88.26	3.19	0.34
% H Evening	-0.047NS	57.42	5.20	0.69	-0.046NS	57.42	5.20	0.69
H Mean	-0.114NS	72.84	3.76	0.44	-0.114NS	72.84	3.76	0.44
Rainfall	-0.198NS	7.50	8.39	3.06	-0.198NS	7.50	8.39	3.06
Wind V.	-0.174NS	1.32	0.29	0.26	-0.174NS	1.32	0.29	0.26
Sunshine	0.195NS	6.89	1.98	0.76	0.195NS	6.89	1.98	0.76
Pooled data 2022 and 2023
Bugs/Hill	1.00	0.30	0.46	0.84	1.00	0.08	0.12	0.42
T Max	-0.183NS	34.16	2.79	0.48	-0.185NS	34.16	2.79	0.48
T Min	0.058NS	23.75	4.60	0.95	0.057NS	23.75	4.60	0.95
T Mean	-0.033NS	28.96	3.61	0.67	-0.035NS	28.96	3.61	0.67
% H Morning	0.261NS	88.08	4.05	0.43	0.263NS	88.08	4.05	0.43

Table text extracted from the original PDF; superscript letters are shown inline. The original table image is below.

View original table image

↗ Enlarge

Table 3Close ×

Pearson’s Correlation Matrix of Rove Beetle Population Dynamics with Abiotic Factors at Korsand

Open full-size image ↗

Original paper tableSource PDF · page 4 ↗

2022	—	—	—	—	—	—	—	—
Parameter	B. /Hill	Mean	Std. D	Std. Error	% P	Mean	Std. D	Std. Error
Bugs/Hill	1.00	0.24	0.36	0.74	1.00	0.06	0.09	0.37
T Max	-0.377NS	34.30	3.55	0.61	-0.377NS	34.30	3.55	0.61
T Min	-0.043NS	23.39	4.88	1.01	-0.044NS	23.39	4.88	1.01
T Mean	-0.192NS	28.84	4.03	0.75	-0.193NS	28.84	4.03	0.75
% H Morning	0.401*	87.90	6.84	0.73	0.401*	87.90	6.84	0.73
% H Evening	0.621**	53.42	10.32	1.41	0.621**	53.42	10.32	1.41
H Mean	0.561**	70.66	8.15	0.97	0.561**	70.66	8.15	0.97
Rainfall	0.119NS	3.75	6.45	3.33	0.119NS	3.75	6.45	3.33
Wind V.	-0.228NS	1.23	0.30	0.27	-0.229NS	1.23	0.30	0.27
Sunshine	0.252NS	5.78	1.70	0.71	0.252NS	5.78	1.70	0.71
2023
Bugs/Hill	1.00	0.22	0.33	0.71	1.00	0.05	0.08	0.36
T Max	0.117NS	34.02	2.62	0.45	0.117NS	34.02	2.62	0.45
T Min	0.190NS	24.11	4.54	0.92	0.190NS	24.11	4.54	0.92
T Mean	0.167NS	29.07	3.49	0.65	0.168NS	29.07	3.49	0.65
% H Morning	-0.191NS	88.26	3.19	0.34	-0.192NS	88.26	3.19	0.34
% H Evening	-0.020NS	57.42	5.20	0.69	-0.020NS	57.42	5.20	0.69
H Mean	-0.095NS	72.84	3.76	0.44	-0.096NS	72.84	3.76	0.44
Rainfall	-0.154NS	7.50	8.39	3.06	-0.154NS	7.50	8.39	3.06
Wind V.	-0.170NS	1.32	0.29	0.26	-0.170NS	1.32	0.29	0.26
Sunshine	0.161NS	6.89	1.98	0.76	0.161NS	6.89	1.98	0.76
Pooled data 2022 and 2023
Bugs/Hill	1.00	0.23	0.35	0.72	1.00	0.06	0.09	0.36
T Max	-0.187NS	34.16	2.79	0.48	-0.188NS	34.16	2.79	0.48
T Min	0.073NS	23.75	4.60	0.95	0.073NS	23.75	4.60	0.95
T Mean	-0.025NS	28.96	3.61	0.67	-0.026NS	28.96	3.61	0.67
% H Morning	0.263NS	88.08	4.05	0.43	0.264NS	88.08	4.05	0.43
% H Evening	0.522**	55.42	6.10	0.82	0.523**	55.42	6.10	0.82
H Mean	0.444*	71.75	4.79	0.57	0.445*	71.75	4.79	0.57
Rainfall	-0.046NS	5.63	5.76	2.43	-0.046NS	5.63	5.76	2.43
Wind V.	-0.216NS	1.28	0.28	0.25	-0.215NS	1.28	0.28	0.25
Sunshine	0.239NS	6.34	1.59	0.63	0.238NS	6.34	1.59	0.63

Table text extracted from the original PDF; superscript letters are shown inline. The original table image is below.

View original table image

↗ Enlarge

Table 2Close ×

Pearson’s Correlation Matrix of Rove Beetle Population Dynamics with Abiotic Factors at Shuats

Open full-size image ↗

Original paper tableSource PDF · page 4 ↗

2022	—	—	—	—	—	—	—	—
Parameter	B. /Hill	Mean	Std. D	Std. Error	% P	Mean	Std. D	Std. Error
Bugs/Hill	1.00	0.27	0.41	0.79	1.00	0.07	0.10	0.39
T Max	-0.369NS	34.30	3.55	0.61	-0.369NS	34.30	3.55	0.61
T Min	-0.028NS	23.39	4.88	1.01	-0.027NS	23.39	4.88	1.01
T Mean	-0.179NS	28.84	4.03	0.75	-0.179NS	28.84	4.03	0.75
% H Morning	0.392NS	87.90	6.84	0.73	0.392NS	87.90	6.84	0.73
% H Evening	0.621**	53.42	10.32	1.41	0.620**	53.42	10.32	1.41
H Mean	0.558**	70.66	8.15	0.97	0.557**	70.66	8.15	0.97
Rainfall	0.124NS	3.75	6.45	3.33	0.123NS	3.75	6.45	3.33
Wind V.	-0.238NS	1.23	0.30	0.27	-0.237NS	1.23	0.30	0.27
Sunshine	0.245NS	5.78	1.70	0.71	0.244NS	5.78	1.70	0.71
2023
Bugs/Hill	1.00	0.26	0.39	0.76	1.00	0.06	0.10	0.38
T Max	0.111NS	34.02	2.62	0.45	0.111NS	34.02	2.62	0.45
T Min	0.181NS	24.11	4.54	0.92	0.181NS	24.11	4.54	0.92
T Mean	0.160NS	29.07	3.49	0.65	0.159NS	29.07	3.49	0.65
% H Morning	-0.179NS	88.26	3.19	0.34	-0.179NS	88.26	3.19	0.34
% H Evening	-0.026NS	57.42	5.20	0.69	-0.025NS	57.42	5.20	0.69
H Mean	-0.094NS	72.84	3.76	0.44	-0.093NS	72.84	3.76	0.44
Rainfall	-0.164NS	7.50	8.39	3.06	-0.163NS	7.50	8.39	3.06
Wind V.	-0.173NS	1.32	0.29	0.26	-0.173NS	1.32	0.29	0.26
Sunshine	0.173NS	6.89	1.98	0.76	0.173NS	6.89	1.98	0.76
Pooled data 2022 and 2023
Bugs/Hill	1.00	0.26	0.40	0.78	1.00	0.07	0.10	0.39
T Max	-0.183NS	34.16	2.79	0.48	-0.184NS	34.16	2.79	0.48
T Min	0.074NS	23.75	4.60	0.95	0.074NS	23.75	4.60	0.95
T Mean	-0.023NS	28.96	3.61	0.67	-0.023NS	28.96	3.61	0.67
% H Morning	0.260NS	88.08	4.05	0.43	0.260NS	88.08	4.05	0.43
% H Evening	0.515**	55.42	6.10	0.82	0.516**	55.42	6.10	0.82
H Mean	0.438*	71.75	4.79	0.57	0.439*	71.75	4.79	0.57
Rainfall	-0.050NS	5.63	5.76	2.43	-0.048NS	5.63	5.76	2.43
Wind V.	-0.216NS	1.28	0.28	0.25	-0.216NS	1.28	0.28	0.25
Sunshine	0.238NS	6.34	1.59	0.63	0.239NS	6.34	1.59	0.63

Table text extracted from the original PDF; superscript letters are shown inline. The original table image is below.

View original table image

↗ Enlarge

Table 1Close ×

Pearson’s Correlation Matrix of Rove Beetle Population Dynamics with Abiotic Factors at Palpur

Open full-size image ↗

Original paper tableSource PDF · page 3 ↗

Conclusion

The study revealed that the population dynamics of rove beetles (Paederus fuscipes) were significantly influenced by environmental factors such as temperature, relative humidity, rainfall, wind velocity, and sunshine hours. Peak population activity was observed in the third week of October, followed by a decline in early November. The findings underscore the importance of rove beetles as effective natural enemies in integrated pest management (IPM) strategies, particularly in rice ecosystems. The significant correlations observed between beetle populations and key weather parameters further highlight their role in regulating pest populations. Encouraging the conservation of these natural enemies through habitat management and reduced pesticide usage can enhance sustainable pest control, reducing reliance on chemical interventions and promoting ecological balance.

Acknowledgement

AcknowledgmentsWe gratefully acknowledge the financial support provided for this research through the Mahatma Jyotiba Phule Research Fellowship (MJPRF) scheme granted by the Autonomous Institute of the Other Backward Class Bahujan Welfare Department, Government of Maharashtra, India. Also, thankful to SHUATS, Prayagraj, U.P. for providing all the essential facilities, No conflict of interests.

Figures and tables from the paper

↗ Enlarge

Figure 1Close ×

Seasonal Population Trends of Rove Beetle During Kharif 2022 and 2023 with Combined Data Analysis

Open full-size image ↗

Original paper figureSource PDF · page 3 ↗

1. Atwal AS, Choudhary JP, Ramjan M. Studies on the seasonal abundance of insects on light trap at Ludhiana. J Res Agric Univ. 1969;6:186-196.
2. Bhattacharyya B, Basit A, Saikia DK. Parasitoids and predators of rice insect pests of Jorhat district of Assam. J Biol Control. 2006;20(1):37-44.
3. De Bach P. Biological control by natural enemies. Cambridge: Cambridge University Press; 1974. p. 323.
4. Dhurva D, Soni R. Influence of climatic factors on population dynamics of insect pests and their natural enemies in rice ecosystem. J Entomol Res. 2020;44(3):445-453.
5. Grist DH, Lever RJ. Pests of rice. London: Longmans, Green; 1969.
6. Gurr GM, Lu Z, Zheng X, Xu H, Zhu P, Chen G, Heong KL. Multi-country evidence that crop diversification promotes ecological intensification of agriculture. Nat Plants. 2016;2(3):16014.
7. Meena SC, Sharma RK, Singh G. Role of predatory beetles in rice pest management: An ecological approach. Int J Pest Manag. 2019;65(2):98-105.
8. Pathak M, Garg DK, Jeswani MD, Singh RN. Farmers participatory IPM in Basmati rice at Sarurpur-A case study. Ann Pl Protec Sci. 2002;10(2):386-388.
9. Pathak M, Riba T, Shakywar RC, Sah D, Mishra BP, Singh MP. Rice ecosystem and their integrated management. In: 7th National Extension Education Congress-2014, Translational Research Extension for Sustainable Small Farm Development; 2014 Nov 0811; ICAR Complex for NEH Region, Umiam, Meghalaya; c2014. p. 103.
10. Sharma AK, Barche S, Mishra PK. Pest and predatory insect species inhabiting paddy ecosystem in Jabalpur, Madhya Pradesh collected with the help of light traps. Pest Manag Econ Zool. 2010;18(12):125-133.
11. Vinoth Kumar R. Seasonal abundance and biological control potential of rove beetles in rice ecosystems. Asian J Agric Sci. 2014;6(2):80-86.
12. Way MJ, Heong KL. The role of biodiversity in the dynamics and management of insect pests of tropical irrigated rice -a review. Bull Entomol Res. 1994;86:567-587.