Survey and seasonal abundance of major insect pests in the maize fields of Punjab, Pakistan

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The objective of this study was to survey the species composition, relative abundance, and population dynamics of arthropod insect pests in maize crop in Punjab, Pakistan. A two-year (2018–2019) survey of insect pests’ species biodiversity in field maize crops was carried out in two districts (Kasur and Lahore). A total of 49 pest species belonging to 45 genera, 27 families, and 6 orders were recorded in this study. Noctuidae dominated over the other pest families, constituting 49.17% of the total pests catch. Fall armyworm, Spodoptera frugiperda (J.E. Smith) was found to be the most dominant species, constituting 18.51% of the sampled individuals. Moreover, the estimated pest species richness from both districts was 94%. While, the diversity indices (Shannon-Weiner and Simpson) revealed non-significant differences in arthropod pest communities at six selected sites. Using the Menhinick and Margalef indices suggested higher species richness in the Lahore district. Overall, the pests population densities were consistently fluctuated throughout both cropping seasons; peaking in April-May and reaching the lowest levels in June-July. Spearman's rank correlation analysis indicated a negative association between insect abundance and temperature while, non-significant correlation was found with humidity in both districts. These findings can help to develop sustainable pests’ control strategies, with implications both at local and global scale in maize growing areas. Noctuidae fall armyworm diversity indices species richness pest management maize crops Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Maize ( Zea mays L.) is one of the most cultivated cereals all over the world for food security due of its adaptability to many agro-ecological settings (Chirinos et al. 2024 ). It provides food for human, animal feed and fodder, and industrial raw materials including breading making, corn starch, and corn oil (Yaqoob et al. 2019 ). It is cultivated globally with an annual production of over one billion metric tons (Goodkind et al. 2023 ). The global maize cultivated area comprises 197 million hectares. It is grown in both developed world and emerging economics, including 165 countries across the Americas, Asia, Europe, and Africa. The USA accounts for approximately 31.54% of global maize production, followed by China (23.37%) and Brazil (10.28%), European Union (4.86%) and Argentina (4.45%) (Statica 2024 ). Maize is Pakistan's third most important cereal crop, following wheat and rice (Ali et al. 2020 ). It is cultivated on 4.8% of the total cropped land and adds 3% value added to agriculture and 0.7% to the country's GDP (Riaz et al. 2024 ). Pakistan has been ranked 22nd in terms of cultivated area and 68th in yield from the maize producing countries (Akhtar et al. 2024 ). Pakistan's contribution to global maize production is relatively low, accounting for only 0.85% of overall output. Furthermore, maize in Pakistan has different applications, with the majority used by the poultry sector (65%), followed by wet milling (20%), silage (10%), and food production (5%) (Gul et al. 2021 ). Maize was grown on 1.4 million hectares in Pakistan between 2020 and 2021, yielding 8.465 million tons (Government of Pakistan 2022 ). The primary contribution to maize production comes from 2 geographical zones of Pakistan; 12 districts of the Punjab (77%) and 11 districts of the Khyber Pakhtunkhwa (22%). Sindh and Baluchistan contribute less than 1% each to the overall production (Rizwanullah et al. 2023 ). In Pakistan, there are two main cropping seasons for maize cultivation, Kharif and Rabi. Kharif sowing begins from April to June and harvests in October to December. Whereas, the cultivation of Rabi season begins from October to December with the harvest from April to May (Abdullah et al. 2022 ). Due to involvement of multinationals in the country, the spring maize crop cultivation has significantly increased (Sultan et al. 2023 ). Although maize is a crop of significant economic value, it is affected by a number of diseases caused by bacteria, fungi, viruses, and insect pests (Savary et al. 2019 ). Globally, 10% of maize yield is lost every year by biotic factors, especially insect pests (Waqas et al., 2021 ). A wide range of pests’ attacks maize crop at its different phenological stages (Chisonga et al. 2023 ). This crop is susceptible to damage from 140 different species of insect pests including maize aphids, thrips, shoot fly, stem borers, fall armyworm, corn earworm, and corn leafhopper causing varying degrees of damage (De Groote 2002 ). Out of 140 insect pests’ species, only twelve constitute major pests of maize, causing damage from sowing to harvesting and especially under storage conditions (Nabeel et al. 2018 ; Khan et al. 2022 ). Different researchers from Pakistan have reported diverse fauna of pests from maize crops (Farid et al. 2007 ; Arifie et al. 2019 ; Sikandar et al. 2021 ; Hamza et al. 2023 ; Khan et al. 2024 ). All of these pests cause significant crop damage by sucking plant sap or chewing the various components, causing the transfer of several diseases in maize (Soujanya et al. 2024 ). In Pakistan, maize aphid, stem borer and fall armyworms are reported to be the primary pest of maize crops responsible for major yield losses (Khan et al. 2024 ). Yield loss can reach to an economic injury level through the lack of control measures and strategies. Understanding the biodiversity and population fluctuations these insect pests is essential to develop effective pest management strategies in maize crops (He et al. 2019 ). The diversity of arthropod communities in an area is regulated by multiple factors including crop management practices, crop phenology, surrounding habitat and climatic factors (Vasseur et al. 2013 ). Insects and humans have intertwined faith, particularly through agriculture (Busse et al. 2021 ). Having up-to-date understanding on maize pest biodiversity is critical for tackling entomological issues. It is necessary to stay updated on pest biodiversity in maize fields and associated predatory fauna, as many of these pests serve as natural food sources for a variety of beneficial insects. Many biologists believe that the unexpected loss of some species from the complicated web of life may have significant effects for all of us, even if those consequences are not yet evident to us (Hallmann et al. 2020 ). Maize agronomists strive to keep insect populations below economic threshold levels (ETL) in order to protect crop yields while also protecting the intricate food web that supports many dependent species, which is critical for maintaining a balanced and sustainable ecosystem (Phani et al. 2024 ). The detailed diversity evaluation of insect pests in maize crops is scarce in Pakistan and very limited reports have mainly targeted only a single pest species. Since the Punjab Province is Pakistan's main maize-producing region, no comprehensive data is available on the arthropod pests affecting this crop. Objectives The present study aims to survey and document the species composition and seasonal abundance of major insect pests from the maize crop fields of Punjab, Pakistan. This work can assist researchers and farmers in developing target control methods for effective Integrated Pest Management (IPM) programs of maize crop in Pakistan. Materials and methods Study areas This study was carried out in two maize-growing districts of Punjab, Pakistan: Kasur and Lahore, during 2018–2019. To survey pests’ data, three sites were selected from each district. These sites from Kasur district are Khudian Khas-K1 (30.9906° N, 74.2708° E), Chunian-K2 (30.9698° N, 73.9712° E), and Pattoki-K3 (31.0249° N, 73.8479° E), whereas Mustafabad-L1 (30.8903° N, 73.4998° E), Pakki Haveli-L2 (31.1188° N, 74.3302° E), and Khana Nou-L3 (31.4731° N, 74.3617° E) were three sites chosen from Lahore district. Each district's research area (8094 m 2 ) was subdivided into four sub-sites and three plots each (674 m 2 ). The average distance among study sites in Kasur was 20 to 25 km, and 25 to 30 km in Lahore district (Fig. 1 ). The two districts themselves were separated by an average of 70 km. The experimental areas were subjected to standard agricultural practices by the farmers. Throughout the study period, these areas were kept entirely free from insecticide applications. Field management practices The seedbed was prepared using one deep plow and followed by two cultivations using a tractor-mounted cultivator during both cropping seasons 2018 and 2019. The hybrid maize variety “Pioneer-P2848W” was chosen and the maize was sown between 14th and February 25th in both 2018 and 2019. Maize was manually sown with ridges spaced 75cm apart and with a plant-to-plant distance of 30cm. Standard N treatments were followed for nitrogen fertilizer application and 172 kg per hectare of phosphorous was applied. During the whole cycle of the maize crop, 12 irrigations (especially water-need-based stages like tasseling, silking, cob, and grain development) were applied through a manual tube well. Manual weed management was applied using hand picking and hoeing. No herbicide was applied at any experimental sites. Notably in 2018, the study sites in the Kasur district were characterized by a monoculture of maize, while in 2019, they were surrounded by Alfalfa. Conversely, in the Lahore district, the study sites were surrounded by Alfalfa in 2018 and maize monoculture in 2019. Sampling and estimation of arthropod pests Pests in maize fields can be found almost anywhere because many are ground dwellers, while others live in the lower regions of plants, the middle, and even the top of plants near the canopy. As a result, several methods such as sweep netting, visual counting, hand picking, beat-sheets, and pitfall trapping were utilized to capture arthropods during different phenological stages of the maize crop i.e., vegetative, tasseling, silking and maturity. The sampling methods differed in their capture efficiency in terms of species richness and family composition. All samples were collected twice a day on selected date i.e., early in the morning and late in the evening. Besides, the meteorological data (air temperature, rainfall and humidity), site information, and collector name were recorded for data analysis. Sweep netting Sweep net sampling was used to collect flying and mobile arthropod pests (aphids, thrips, leafhoppers, spider mites, whitefly, adults of armyworms, and stem borers) in maize crops, especially for canopy-dwelling arthropods. Each sweep involved gently swinging the net back and forth numerous times to remove insects from the plants. At each location within the sites, twenty-five hits (gauze net with 30cm diameter) were performed by walking through each plot between 10 am to 4 pm. The collected insects were carefully transported from the net into labeled containers and temporarily stored before further processing for identification. The collected pest fauna was counted to identify and calculated their abundances in maize experimental sites. Pitfall traps Pitfall traps are the best known and most often used inventory method in agroecosystems (Ahmed et al. 2023 ). Pitfall traps were installed for the monitoring of ground dwelling arthropods that are active near the soil surface especially for Carabidae, Saltatoria, and Elateridae families. For the ground-dwelling pest collection, 25 pitfalls (7-inch length and 3-inch width) were installed at each sub-site at 2.5 meters apart. The pitfall traps were inserted in the ground so that lip was flushed with the soil. One-third of each jar was filled with a mixture of ethyl acetate (70%) and water (30%). A few drops of the liquid detergent were also added in each pitfall to lower the surface tension. All pitfall traps were covered with transparent plexi-glass roof supported 5cm above the trap with four nails. During each sampling period, pitfall traps were emptied every two days by filtering out the pest specimens through a strainer. The collected pests were placed in vials with 75% ethanol. Yellow pan traps For the actively moving small pests like winged aphids, locally made yellow pan traps (15 × 24 cm with 5 cm depth) were installed 15cm above the canopy. These pans were exposed for 24 hours to trap insect pests through yellow color attraction. Also, 4–5 yellow sticky pans coated with the grease were used in each maize field plot. Beating sheet The beating sheet method was used to catch canopy inhabiting pests maize pests from field and surrounding margins. During this method, inverted white umbrella was placed beneath the maize plants and foliage was gently shaken. This technique was especially used during the tasseling and maturity growth stages of maize crops. Visual counting and hand-picking To enhance the accuracy of arthropod pest catch, visual counting and hand-picking methods were also used. The visual counting approach was used to calculate the total number and relative abundance of several insect pests such as maize weevil, corn earworm, European corn borer etc. Twenty-five plants were selected from each corn field plot and studied using a Randomized Complete Block Design (RCBD). Counting was performed on the selected plants' leaves (upper, middle, and lower) using the naked eye or a magnifying glass (4x). Many active fliers escaped away quickly upon approach, thus many were computed visually. Sample preservation and morphological identification Collected specimens from each site were placed in 25 ml glass vials filled with a solution of 80% ethanol and a few drops of glycerin, depending on the size of the catches. Each vial was clearly labeled with the collection location, date, and such other important information. Initially, all collected specimens were frozen. Subsequently, they were transferred to the Agricultural Entomology and Toxicology Laboratory, Department of Zoology, Government College University, Lahore for further counting and identification. To remove field debris, the specimens were briefly rinsed with 75% alcohol before final preservation in 95% ethanol and storage in a refrigerator. The collected specimens were carefully identified to the lowest possible taxonomic level by examining their morphological characters under a stereo zoom microscope (IRMECO GmbH, model IM-SZ-500) equipped with a digital camera (Cannon Power Shot G9). The pest fauna was identified consulting available keys and catalogues such as Ortega ( 1987 ); O'Day et al. ( 1998 ); Steffey et al. ( 1992 ); Kumar et al. ( 2014 ), and Ekman & Duff ( 2015 ). The immature pests were identified to the genus level. Additionally, arthropod pests’ data available on the Barcode of Life Data Systems (BOLD) was also consulted to assist in identification. Voucher specimens were deposited in the Stephenson Natural History Museum, Government College University Lahore, Pakistan for further reference. Geographical coordinates, elevation, and other ecologically relevant data such as temperature (LM-8000), humidity (R6001 Thermo-Hygrometer), and rainfall were collected using a portable GPS device (Garmin model 010-02256-00) and an environmental data recorder. Biodiversity and similarity measures To estimate the total species richness of pests’ fauna in both districts, two of the most widely used estimators, Chao 1 and Chao 2, were computed using the Estimate S program. Chao 1 is considered a minimum estimator of species richness, particularly effective when the number of species represented by single-tones and double-tones species is high (Chao et al. 2017 ). \({S_1}={S_{obs}}+\frac{{{F_1}^{2}}}{{2{F_2}}}\) (1) Where: S obs was the number of species in the sample, F 1 the number of single tone species in the sample and F 2 is the number of double tone species in the sample. $${S_2}={S_{obs}}+\frac{{{Q_1}^{2}}}{{2{Q_2}}}$$ 2 Where: S obs was the number of species in the sample, Q 1 the number of single tone species in the sample and Q 2 is the number of double tone species in the sample. The diversity of pests at different selected sites was analyzed using two widely used diversity indices: the Shannon-Wiener index, which is sensitive to change in the abundance of rare species within the community, and the Simpson index, which is more sensitive to the most abundant species (Morris et al. 2014 ). The Shannon-Wiener index ( \(H'\) ) was calculated using the following formula: $$H'= - \sum {{P_i}\log {p_i}}$$ 3 Where \({p_i}=\frac{n}{N}\) Species richness was also computed using Margalef Index, based on the relationship between species richness (S) and total number of individuals observed (N). $$d'=\frac{{S - 1}}{{\ln N}}$$ 4 Menhinick index was used to assess the relationship between the number of species present in the sample and the total number of individuals collected. Evenness indices, described how evenly the species are distributed in the sample. A high evenness index value indicates that all species in the sample are equally distributed. Conversely, a decreasing evenness value towards zero indicates that the relative abundance of species diverged away from evenness. The modified Hill’s Ratio (E-5) is the most reliable evenness index as it is independent of number of the species in the sample. $$E5=(1/D) - 1/{e^{H'}} - 1$$ 5 Where: D = Simpson's index, and H = Shannon-Wiener index All the diversity indices were computed using the statistical software SPDIVERS.BAS. The degree of the association of the sampling sites was found using cluster analysis. It is a useful data reduction technique that can be helpful in the analysis of grouping of the objects. The cluster analysis was performed through MSVP. Ver. Similarity estimates were analyzed using the unweight pair group method with arithmetic mean (UPGMA) and the resulting clusters were represented as dendrograms. The daily rainfall and temperature data were recorded in the selected sampling locations. Statistical analysis Before further analysis, obtained data in this study was assessed for normality using the Shapiro-Wilk test. Since non-significant difference (p > 0.05) was found in arthropod pest populations at selected locations during both cropping seasons (2018–2019), the data was combined for further analysis. Species accumulation curves were generated using the SPDIVERS.BAS program to estimate whether the sampling effort was sufficient to estimate the total number of species (Ludwig and Reynolds 1988 ). Additionally, a logarithmic tendency curve was generated to visualize the increase in the number of species. As the biodiversity data collection is labor-intensive and time-consuming, a small portion of the community is represented by rare species, often which are mostly single tones, might remain undetected by most surveys. Spearman's rank correlation coefficient was used to analyze the correlation among species abundance in all fields and meteorological parameters (temperature and relative humidity). Results Arthropod pests’ communities in maize plots were investigated throughout the 2018 and 2019 cropping seasons, and the cumulative number of arthropod pests was analyzed. A total of 97671 insect pests representing 49 species belonging to 45 genera, 27 families, and 6 orders were recorded from both Kasur and Lahore districts. Identification of the captured insects revealed that 32525 individuals were immature, and their identification was limited to the genus level due to the unavailability of suitable keys for juvenile stages. The remaining 65175 mature individuals were classified to the species level (Table 1 ). Generally, family Noctuidae was the most abundant one among the major pests’ families affecting maize crops (49.17%), followed by Crambidae (14.94%), Thripidae (7.59%), Aphididae (6.28%), Cicadellidae (4.25%), Acrididae (3.30%), Muscidae (2.46%), Delphacidae (2.16%), Curculionidae (1.75%), Lygaeidae (1.09%), Pentatomidae (1.02%), and the last was Chrysomelidae (0.94%). The combined proportion of all remaining families was less than 4.27% of the total catch. The fall armyworm, Spodoptera frugiperda (J.E. Smith, 1797) (Family: Noctuidae) was the most dominant species; constituting (18.51%) of the total pests’ catches, followed by Chilo partellus (Swinhoe), (14.94%), and Helicoverpa armigera (Hübner), (13.84%) (Fig. 2 ). Table 1 The relative abundance (%) of pests associated with maize crops of two districts of the Punjab, Pakistan. Order Family Species Kasur Lahore Total R.A. % Coleoptera Chrysomelidae Monolepta signata (Olivier, 1808 ) 12 17 29 0.03 Chaetocnema pulicaria (Melsheimer) 433 379 812 0.83 Diabrotica virgifera virgifera LeConte, 1868 6 17 23 0.02 Oulema sp. 23 29 52 0.05 Coccinellidae Epilachna varivestis (Mulsant, 1850) 55 34 89 0.09 Curculionidae Myllocerus undecimpustulatus (Faust, J. 1891) 354 412 766 0.78 Sitophilus zeamais (Motschulsky, 1855) 237 279 516 0.53 Anthonomus grandis (Boheman, 1843) 14 21 35 0.04 Myllocerus sp. 549 610 1159 1.19 Elateridae Agriotes sp. 77 43 120 0.12 Scarabaeidae Oxygrylius ruginasus (Le Conte, 1856) 122 99 221 0.23 Digitonthophagus gazelle (Fabricius, 1787) 221 311 532 0.54 Phyllophaga sp. 0 37 37 0.04 Tenebrionidae Tenebrionidae sp. 11 24 35 0.04 Meloidae Mylabris pustulata (Thunberg, 1821) 112 198 310 0.32 Meloidae sp. 13 0 13 0.01 Diptera Muscidae Atherigona soccata Rondani 499 522 1,021 1.05 Atherigona orientalis (Schiner) 511 871 1,382 1.41 Hemiptera Aphididae Rhopalosiphum maidis (Fitch, 1856) 3589 2544 6,133 6.28 Cicadellidae Cicadulina mbila (Naudé,1924) 2009 2143 4,152 4.25 Delphacidae Peregrinus maidis (Ashmead, 1890) 1134 978 2,112 2.16 Lophopidae Pyrilla perpusilla (Walker, 1851) 312 279 591 0.6 Lygaeidae Oxycarenus laetus (Kirby, 1891) 455 521 976 1 Spilostethus saxatilis (Scopoli, 1763) 12 78 90 0.09 Coreidae Cletus pugnator (Fabricius, 1787) 123 55 178 0.18 Order Family Species Kasur Lahore Total R.A. % Pentatomidae Eysarcoris ventralis (Westwood, 1837) 425 311 736 0.75 Nezara viridula (Linnaeus, 1758) 69 102 171 0.18 Bagrada hilaris (Burmeister, 1835) 78 12 90 0.09 Pseudococcidae Heterococcus nudus (Green, 1926) 0 11 11 0.01 Pyrrhocoridae Dysdercus cingulatus (Fabricius, 1775) 231 112 343 0.35 Lepidoptera Crambidae Chilo partellus (Swinhoe) 7002 7591 14,593 14.94 Erebidae Laelia suffusa (Hampson, 1893) 258 123 381 0.39 Geometridae Geometridae sp. 12 121 133 0.14 Hesperiidae Pelopidas mathias (Fabricius, 1798) 49 37 86 0.09 Noctuidae Agrotis ipsilon (Hufnagel) 155 287 442 0.45 Busseola fusca (Fuller, 1901) 3512 2981 6,493 6.65 Helicoverpa zea (Boddie) 4377 5124 9,501 9.72 Helicoverpa armigera (Hübner) 6578 6945 13,523 13.84 Spodoptera frugiperda (J.E. Smith, 1797) 9,875 8,214 18,089 18.51 Nolidae Earias insulana (Boisduval, 1833) 255 143 398 0.41 Earias vittella (Fabricius, 1794) 70 119 189 0.19 Orthoptera Acrididae Acrida willemsei (Dirsh, 1954) 469 533 1,002 1.03 Hieroglyphus perpolita (Uvarov, 1933) 1234 987 2,221 2.27 Pyrgomorphidae Chrotogonus trachypterus (Blanchard, 1836) 70 112 182 0.19 Tettigoniidae Oxyiachinensis sp. 211 110 321 0.33 Thysanoptera Thripidae Thrips tabaci (Lindeman, 1889) 1125 1349 2,474 2.53 Frankliniella williamsi Hood, 1915 2359 2578 4,937 5.05 Total 49297 48403 97700 100 The analysis of species accumulation curves (pooled for two years data) for insect pests in the two districts is shown (Fig. 3 ). The number of trappable insect pest species increased continuously with the increase of sample size. The curve initially had a steep slope, indicating a rapid increase in the number of species as more individuals were sampled. However, the curve did not reach an asymptote. The Chao 1 and Chao 2 estimator were used to provide a more accurate assessment of the total species diversity in each area. Based on the Chao-2 estimate, the estimated species richness was 45.72 and 47.42 at Kasur and Lahore districts, respectively. This suggests that the ratio of observed to estimate the species number was 95% and 97% for districts Kasur and Lahore, respectively. According to the species completeness data, at least 5% and 3% of species in Kasur and Lahore, respectively, are expected present in the study area but were not captured during this sampling (Table 2 ). Table 2 Species diversity and inventory completeness for insect pests collected from Kasur and Lahore districts. Parameters District Kasur District Lahore Number of specimens 49297 48403 Observed richness 43 46 No of singletons 5 4 No of Doubletons 5 6 Estimated Species Richness Chao 1 45.5 47.33 Chao 2 45.72 47.42 % completeness 95 97 Arthropod pest biodiversity was analyzed using four different indices. District Kasur had the total pest capture (49297) compared to Lahore (48403). Pest richness as measured by the Menhinick and Margalef indices did not significantly differ between both districts. Similarly, diversity indices, including the Shannon-Wiener Index and Simpson's Index, showed non-significant difference between Kasur and Lahore (Table 3 ). The modified Hill ratio (E-5), which reflects the dominance of specific pest species, displayed almost uniform values for both districts, suggesting a relatively similar degree of dominance in pest species between the two regions. A cluster analysis based on the similarity in pest species composition showed clear groupings of the study sites in each district. Sites K1 and K2 from Kasur formed a distinct clusters with 93% similarity in pest species, while sites L2 and L3 in Lahore showed another cluster with 89% similarity. However, site K3 displayed 92% similarity with other Kasur sites (K1-K2), and site L1 from Lahore displayed 87% similarity with the other sites (L2-L3) (Fig. 4 ). Table 3 Total abundance, richness, diversity, and evenness indices for the insect pests collected from Kasur and Lahore districts in 2018 and 2019 seasons. Parameters Study areas District Kasur District Lahore Number of Specimens 49297 48403 Richness Indices Margalef Index 4.07 4.17 Menhinick Index 0.203 0.209 Diversity Indices Shannon-Wiener Index 3.87 3.91 Simpson's Index 0.999 0.994 Evenness index (E-5) 0.903 0.904 Population dynamics of pest species in Kasur and Lahore districts during 2018 and 2019 are presented in (Fig. 5 ). Both districts exhibited similar population fluctuation pattern. Generally, the density of pest populations showed uni-modal seasonal pattern, with peak activity in April. However, the lowest densities were in June and July for both Kasur and Lahore districts during both cropping seasons (2018–2019). There was a strong positive correlation between the population densities of fall armyworm, with temperature (r = 0.750, p < 0.05), whereas maize aphid population densities showed negative correlation with temperature (r = -0.714, p 0.05). Maize stem borer showed weaker correlations with temperature (r = 0.429, p > 0.05) and humidity (r = -0.297, p > 0.05). Furthermore, maize earworm showed significant positive correlation with temperature (r = 0.690, p 0.05) and rainfall (r= -0.359, p < 0.05) (Table 4 ). Figure 6 illustrated the major pests of maize crops from the study districts. Table 4 The association of the major pest species with temperature, rainfall and humidity during maize growing seasons (2018–2019). Major Pest Temperature Rainfall Humidity Fall armyworm r = 0.750* r = -0.175 r = -0.279 p = 0.017 p = 0.699 p = 0.509 Stem borer r = 0.429 r = -0.297 r= -0.286 p = 0.286 p = 0.755 p = 0.487 Maize earworm r = 0.690 r = -0.468 r= -0.359 p = 0.020 p = 0.516 p= -0.495 * Correlation is significant at the 0.05 level Discussion The study provides baseline data on major insect pests of maize crops of Punjab, Pakistan. Maize, grown year-round is susceptible to various insect pests from seedling to harvest (Khan et al. 2022 ). The diversity, seasonal abundance, and population dynamics of these pests vary monthly. Over a two year survey in two major maize growing districts, 97671 arthropod pests from 49 species were recorded. The family Noctuidae was the most dominant at all six sampling sites, constituting nearly 50% of the total pest catch. Within this family, S. frugiperda was the most prevalent pest (18.51%); followed by Chilo partellus (14.94%), Helicoverpa armigera (13.84%) and H. zea (Boddie) (9.72%). The current findings align with Urge et al. ( 2020 ), who reported similar dominant pests in Ethiopian maize fields. Maize thrips ( Frankliniella williamsi ), maize aphids Rhopalosiphum maidis , Atherigona soccata , maize leafhopper ( Cicadulina mbila) and Chaetocnema pulicaria constituted the sucking pest complex; targeting maize vegetative stages by extracting the plant sap (Paul et al. 2020). Furthermore, FAW; corn earworm ( H. armigera & H. zea) and corn root worms ( Agrotis ipsilon collectively form the chewing pest complex. They target the maize at different phenological stages by chewing different parts or by transmitting different types of bacteria and viruses (Soujanya et al. 2024 ). The sucking pests like leaf hopper, C. mbila usually attack the early vegetative and tasseling stages of maize. Maize aphids are widely prevalent pests; sucking the juice of the soft vegetative parts throws their mouthparts and serves as vector for the virus transmission (Oberemok et al. 2023 ). Notably, FAW is a major invasive pest species of maize globally, infesting all stages of maize crops (Dassou et al. 2021 ). In our study, it was recorded as the dominant pest, comprising 18.51% of the total pest catch. FAW, a polyphagous insect also attacks other economic cash crops like rice, wheat, cotton, and sorghum (Mlambo et al. 2023). It causes a severe damage to the vegetative and reproductive parts of maize plants. Initially, young FAW larvae feed near the ground level. As they mature, the larvae start creating holes in leaves or stems from the outside inward. Mature leaves may have three to four rows of tiny holes (Day et al. 2017 ). Larval densities ranging from 0.2–0.8% per plant at the late whorl stage can result in 5–20% yield losses (Makgoba et al. 2021 ). FAW larvae hide within the maize funnel during the day and feed at night (Day et al. 2017 ). The presence of FAW on maize crops in Pakistan has been reported by other researchers. Gilal et al. ( 2020 ) reported 100% damage to fodder maize in Sindh district. Similarly, Ibrahim et al. (2022) found FAW in maize fields in Kasur and Lahore districts, with peak infection rates reaching 19.39%. Notably, damage was more severe at the edges of the maize fields compared to the central regions. This could be due to the probable migration of FAW from neighboring crops such as wheat and rice. In addition to FAW, the study reported numerous other notable insect pests of maize crops in Pakistan. Maize steam borer, Chilo partellus from the family Crambidae was ranked as the second most abundant pest; constituting 14.94% of the total pest catches. This pest is globally widespread in maize; causing yield losses up to 42.29% (Guo et al. 2023 ). The larvae are more destructive, tunneling in the stem or stalk after hatching from eggs and hindering ear formation. They can move between plants through the holes made in the lower stem nodes (Srivastava et al. 2016 ). Nabeel et al. ( 2018 ) also identified this species as the main maize pest in Punjab, Pakistan. The Noctuid moths of Helicoverpa armigera (Hübner) (13.84%) and H. zea (Boddie) (9.72%) also caused significant losses in maize crops. These results are agreeable with those reported previously by Wang et al. ( 2023 ) in China. Moreover, Keszthelyi et al. ( 2016 ) reported that H. armigera can reduce average maize ear weight by 13.99%. It is also considered as key pest in agriculture and horticulture in Pakistan. On the other hand, H. zea is a polyphagous insect pest feeds on various crops especially maize. The larvae can damage both cultivated and wild host plants, particularly when feeding the maize ear. Their larvae initially feed on the silk and then move down to the ear tip. Previous studies by Sarwar ( 2023 ) confirmed the presence of H. zea in maize fields of Pakistan. This study documented the presence of other pest species besides the dominant ones discussed earlier. These included maize aphids, grasshoppers, shoot flies, and white grubs. In this study, 96% of the species were successfully captured in both districts during the study period (2018 − 209). The remaining 4% of targeted crop pests might represent rare species, or their activity patterns or timing might have differed from the sampling schedule, leading to their undetected presence. This is evident in from species accumulation curves, which did not reach an asymptote at both districts. Similar findings were reported by Schmidt and Balakrishnan ( 2015 ), suggesting that various insect species may exhibit varying activity times to avoid competition. It is also possible that the methods for collecting insect pest samples weren't sufficient enough to guarantee a 100% complete pest inventory in both research regions. Furthermore, some pest species might only emerge sporadically during cropping seasons, potentially escaping capture during the sample phase. This is consistent with observations by other entomologists studying on the agricultural crops biodiversity. For example, Borges and Brown ( 1999 ) reported 90% completeness in their species inventory of arthropods in Azorean pasture, while Nadeem et al. ( 2023 ) recorded 94% of pest species from the cotton crops in Pakistan. This study revealed a distinct seasonal pattern in pest abundance during the cropping years (2018–2019). The number of insect pests began to increase in mid-March, reaching recorded peak in April. These findings align with the previous research conducted by Khan et al. ( 2022 ) in maize fields in Khyber Pakhtunkhwa, Pakistan. This data not only confirm the presence of predictable seasonal trends in pest populations, but also highlight the resilience of such patterns across various agricultural contexts in the Pakistan. The observed seasonal dynamics and minor changes in diversity indices are most likely caused by a combination of meteorological variables, crop phenology, and agricultural methods. Given how temperature, humidity, and precipitation affect insect communities, it is possible that these climatic factors contributed to the very uniform diversity measurements observed (Vasconcellos et al. 2019). Furthermore, in addition to climatic conditions, other ecological factors, such as soil composition, vegetation structure, and land management methods, may have influenced local insect diversity patterns (Duan et al. 2021 ). The difference between the actual and estimated species richness using Chao-1 and Chao-2 estimator suggests that the sampling methods used to capture insect pests were not enough. It suggests that more intensive sampling efforts needed with additional sampling techniques for better capture of insect pests at both districts. Furthermore, extending the sampling time could also be an option for better pest capture. This approach may assist to capture a wider range of insect pests with diverse activity patterns throughout the day (Montgomery et al. 2021 ). The cluster analysis dendrogram revealed a clear grouping of sampling sites in both districts. This suggests that the pest communities share a similar geographic and environmental range. This observation is further supported by the minimal differences observed in maize field data collected from different sites within each district. Climatic factors significantly impact the diversity of maize pests, influencing their population dynamics, distribution, and interaction with the natural predators (spiders, coccinellids, green lacewing) (Thomson et al. 2010 ). In the present research, FAW showed positive correlation (r = 0.750; p = 0.024) with the temperature consistent with the findings of Soumia et al. ( 2021 ). This might be due to enhanced reproductive rate and development of FAW in warmer conditions. Furthermore, the research revealed that maize stem borer ( Chilo partellus ) populations exhibited a positive correlation with temperature (r = 0.429; p = 0.274) while showing negative correlation with the rainfall (r = -0.106; p = 0.788) and humidity (r = -0.297; p = 0.481) levels. These finding suggest that higher temperatures favor proliferation of stem borer, whereas increase humidity and rainfall potentially due to the disruption of their habitat and life cycle. The results are supported by global research that has shown comparable trends and correlations between temperature, rainfall, humidity, and the abundance of maize stem borer populations (Régnier et al. 2023 ). In contrast to the above trends, maize aphids showed a negative association with temperature and a positive correlation with humidity levels. It is worth noting that the maize aphids observed in the current study were primarily found during the early vegetative phases (March and April) of maize crops. This could be because maize crops' tender stems and leaves produce more sap than mature crop stages (Șimon et al. 2023 ). In general, sucking pests dominated in April and May, while chewing pests were more prevalent from May to June (Murtaza et al. 2019 ). Conclusions The study highlights the vulnerability of maize crop to different insect pest complexes throughout its growing season. Sucking pests attack the early vegetative stages of maize. As the plant proceeds to the next growth stage, chewing pests particularly fall armyworm and maize stem borers attack the vegetative plants. Attributing pest population fluctuations solely to environmental factors can be challenging. Future research could expand on the current study's findings to investigate arthropod diversity for more effective domestic and internationally Integrated Pest Management strategies. Declarations Acknowledges The authors express their gratitude to ORIC, Government College University, Lahore, Pakistan for supporting this project. We also deeply appreciate the farmers in Kasur and Lahore districts for their invaluable assistance with our research. Additionally, we wish to acknowledge the referees for their thorough evaluation and constructive feedback, which greatly aided in the revision of the manuscript. Declarations The authors have no competing interests to declare that are relevant to the content of this article. References Abdullah MH, Ahmad A, Saboor A, Aftab M, Baig IA, Iftikhar M, Hussain J (2022) Climatic variability during cropping seasons in agroecological zones of Pakistan. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4301820","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":331177046,"identity":"4c88bd24-164c-4ae6-93af-3fbc38796301","order_by":0,"name":"Naveed Akhtar","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-5305-7487","institution":"Government College University Lahore","correspondingAuthor":true,"prefix":"","firstName":"Naveed","middleName":"","lastName":"Akhtar","suffix":""},{"id":331177047,"identity":"9c713e29-5274-4a91-892e-c119fcadaf35","order_by":1,"name":"Hafiz Muhammad Tahir","email":"","orcid":"","institution":"Government College University Lahore","correspondingAuthor":false,"prefix":"","firstName":"Hafiz","middleName":"Muhammad","lastName":"Tahir","suffix":""},{"id":331177048,"identity":"8447d411-0471-458a-ab64-818f05f5dd47","order_by":2,"name":"Azizullah Azizullah","email":"","orcid":"","institution":"Government College University Lahore","correspondingAuthor":false,"prefix":"","firstName":"Azizullah","middleName":"","lastName":"Azizullah","suffix":""},{"id":331177049,"identity":"6c5838cd-3fde-4053-895e-107e918b8739","order_by":3,"name":"Aamir Ali","email":"","orcid":"","institution":"Government College University Lahore","correspondingAuthor":false,"prefix":"","firstName":"Aamir","middleName":"","lastName":"Ali","suffix":""},{"id":331177050,"identity":"fee19bfc-d152-4a9e-b2a6-3ccb0f14b5b8","order_by":4,"name":"Rabia Fajar","email":"","orcid":"","institution":"Lahore College for Women University","correspondingAuthor":false,"prefix":"","firstName":"Rabia","middleName":"","lastName":"Fajar","suffix":""},{"id":331177051,"identity":"8fcfdfa4-ca38-4e1e-aaf8-15bb74bdfcac","order_by":5,"name":"Ayesha Muzamil","email":"","orcid":"","institution":"Government College University Lahore","correspondingAuthor":false,"prefix":"","firstName":"Ayesha","middleName":"","lastName":"Muzamil","suffix":""},{"id":331177052,"identity":"c7fb8f18-c020-4575-a977-4a8d1c8eb2be","order_by":6,"name":"Reham Fathy","email":"","orcid":"","institution":"Scientific Research Institute of Plant Protection and Technical Plants","correspondingAuthor":false,"prefix":"","firstName":"Reham","middleName":"","lastName":"Fathy","suffix":""},{"id":331177053,"identity":"4c38d7a6-ec50-4d80-ab2d-2b56e68716fc","order_by":7,"name":"Hend O. Mohamed","email":"","orcid":"","institution":"Scientific Research Institute of Plant Protection and Technical Plants","correspondingAuthor":false,"prefix":"","firstName":"Hend","middleName":"O.","lastName":"Mohamed","suffix":""},{"id":331177054,"identity":"a125a061-9ac7-4578-ac6b-8327335c61a4","order_by":8,"name":"Dilawar Abbas","email":"","orcid":"","institution":"CAAS IPP: Chinese Academy of Agricultural Sciences Institute of Plant Protection","correspondingAuthor":false,"prefix":"","firstName":"Dilawar","middleName":"","lastName":"Abbas","suffix":""}],"badges":[],"createdAt":"2024-04-21 18:26:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4301820/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4301820/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s42690-025-01588-3","type":"published","date":"2025-08-28T15:57:15+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":61090213,"identity":"388ff865-33f7-4bd0-95bf-333e0ef404bc","added_by":"auto","created_at":"2024-07-25 12:57:35","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":528771,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Figures1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4301820/v1/18a66e187ad0a96935cdc9d3.jpg"},{"id":61091427,"identity":"e1a437af-ee73-4214-a1e0-213dc4e55b81","added_by":"auto","created_at":"2024-07-25 13:13:35","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":320670,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Figures2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4301820/v1/dc0a6e995586e2d2bb7eefbb.jpg"},{"id":61090841,"identity":"721ee82f-9f38-4b40-994c-5160130cc6a2","added_by":"auto","created_at":"2024-07-25 13:05:35","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":433852,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Figures3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4301820/v1/21d5d353e32257a1e2d7b00c.jpg"},{"id":61090210,"identity":"06bd02f2-2c5f-4167-b986-af37507bf363","added_by":"auto","created_at":"2024-07-25 12:57:35","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":257734,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Figures4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4301820/v1/3b709ba990c1adfc29e58002.jpg"},{"id":61090843,"identity":"48b3a368-35b2-4614-bbd6-c42f6946d315","added_by":"auto","created_at":"2024-07-25 13:05:35","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":502009,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Figures5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4301820/v1/a094b3687c275d59075f6381.jpg"},{"id":61090214,"identity":"710fd423-57ca-4a5f-bab4-bdcae13abbdb","added_by":"auto","created_at":"2024-07-25 12:57:35","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1114000,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Figures6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4301820/v1/e2c4a049db1f96868ef8654b.jpg"},{"id":90344967,"identity":"2c3e24a9-f6f8-4748-a4d5-429d4eba547f","added_by":"auto","created_at":"2025-09-01 16:08:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4201440,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4301820/v1/fe2ae2bd-2c14-4b96-9054-4761ed698cea.pdf"}],"financialInterests":"","formattedTitle":"Survey and seasonal abundance of major insect pests in the maize fields of Punjab, Pakistan","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMaize (\u003cem\u003eZea mays\u003c/em\u003e L.) is one of the most cultivated cereals all over the world for food security due of its adaptability to many agro-ecological settings (Chirinos et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). It provides food for human, animal feed and fodder, and industrial raw materials including breading making, corn starch, and corn oil (Yaqoob et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). It is cultivated globally with an annual production of over one billion metric tons (Goodkind et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The global maize cultivated area comprises 197\u0026nbsp;million hectares. It is grown in both developed world and emerging economics, including 165 countries across the Americas, Asia, Europe, and Africa. The USA accounts for approximately 31.54% of global maize production, followed by China (23.37%) and Brazil (10.28%), European Union (4.86%) and Argentina (4.45%) (Statica \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Maize is Pakistan's third most important cereal crop, following wheat and rice (Ali et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). It is cultivated on 4.8% of the total cropped land and adds 3% value added to agriculture and 0.7% to the country's GDP (Riaz et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Pakistan has been ranked 22nd in terms of cultivated area and 68th in yield from the maize producing countries (Akhtar et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Pakistan's contribution to global maize production is relatively low, accounting for only 0.85% of overall output. Furthermore, maize in Pakistan has different applications, with the majority used by the poultry sector (65%), followed by wet milling (20%), silage (10%), and food production (5%) (Gul et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMaize was grown on 1.4\u0026nbsp;million hectares in Pakistan between 2020 and 2021, yielding 8.465\u0026nbsp;million tons (Government of Pakistan \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The primary contribution to maize production comes from 2 geographical zones of Pakistan; 12 districts of the Punjab (77%) and 11 districts of the Khyber Pakhtunkhwa (22%). Sindh and Baluchistan contribute less than 1% each to the overall production (Rizwanullah et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In Pakistan, there are two main cropping seasons for maize cultivation, Kharif and Rabi. Kharif sowing begins from April to June and harvests in October to December. Whereas, the cultivation of Rabi season begins from October to December with the harvest from April to May (Abdullah et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Due to involvement of multinationals in the country, the spring maize crop cultivation has significantly increased (Sultan et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Although maize is a crop of significant economic value, it is affected by a number of diseases caused by bacteria, fungi, viruses, and insect pests (Savary et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGlobally, 10% of maize yield is lost every year by biotic factors, especially insect pests (Waqas et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A wide range of pests\u0026rsquo; attacks maize crop at its different phenological stages (Chisonga et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This crop is susceptible to damage from 140 different species of insect pests including maize aphids, thrips, shoot fly, stem borers, fall armyworm, corn earworm, and corn leafhopper causing varying degrees of damage (De Groote \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Out of 140 insect pests\u0026rsquo; species, only twelve constitute major pests of maize, causing damage from sowing to harvesting and especially under storage conditions (Nabeel et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Khan et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Different researchers from Pakistan have reported diverse fauna of pests from maize crops (Farid et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Arifie et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Sikandar et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hamza et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Khan et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). All of these pests cause significant crop damage by sucking plant sap or chewing the various components, causing the transfer of several diseases in maize (Soujanya et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In Pakistan, maize aphid, stem borer and fall armyworms are reported to be the primary pest of maize crops responsible for major yield losses (Khan et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Yield loss can reach to an economic injury level through the lack of control measures and strategies. Understanding the biodiversity and population fluctuations these insect pests is essential to develop effective pest management strategies in maize crops (He et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The diversity of arthropod communities in an area is regulated by multiple factors including crop management practices, crop phenology, surrounding habitat and climatic factors (Vasseur et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInsects and humans have intertwined faith, particularly through agriculture (Busse et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Having up-to-date understanding on maize pest biodiversity is critical for tackling entomological issues. It is necessary to stay updated on pest biodiversity in maize fields and associated predatory fauna, as many of these pests serve as natural food sources for a variety of beneficial insects. Many biologists believe that the unexpected loss of some species from the complicated web of life may have significant effects for all of us, even if those consequences are not yet evident to us (Hallmann et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Maize agronomists strive to keep insect populations below economic threshold levels (ETL) in order to protect crop yields while also protecting the intricate food web that supports many dependent species, which is critical for maintaining a balanced and sustainable ecosystem (Phani et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The detailed diversity evaluation of insect pests in maize crops is scarce in Pakistan and very limited reports have mainly targeted only a single pest species. Since the Punjab Province is Pakistan's main maize-producing region, no comprehensive data is available on the arthropod pests affecting this crop.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eThe present study aims to survey and document the species composition and seasonal abundance of major insect pests from the maize crop fields of Punjab, Pakistan. This work can assist researchers and farmers in developing target control methods for effective Integrated Pest Management (IPM) programs of maize crop in Pakistan.\u003c/p\u003e \u003c/div\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy areas\u003c/h2\u003e \u003cp\u003eThis study was carried out in two maize-growing districts of Punjab, Pakistan: Kasur and Lahore, during 2018\u0026ndash;2019. To survey pests\u0026rsquo; data, three sites were selected from each district. These sites from Kasur district are Khudian Khas-K1 (30.9906\u0026deg; N, 74.2708\u0026deg; E), Chunian-K2 (30.9698\u0026deg; N, 73.9712\u0026deg; E), and Pattoki-K3 (31.0249\u0026deg; N, 73.8479\u0026deg; E), whereas Mustafabad-L1 (30.8903\u0026deg; N, 73.4998\u0026deg; E), Pakki Haveli-L2 (31.1188\u0026deg; N, 74.3302\u0026deg; E), and Khana Nou-L3 (31.4731\u0026deg; N, 74.3617\u0026deg; E) were three sites chosen from Lahore district. Each district's research area (8094 m\u003csup\u003e2\u003c/sup\u003e) was subdivided into four sub-sites and three plots each (674 m\u003csup\u003e2\u003c/sup\u003e). The average distance among study sites in Kasur was 20 to 25 km, and 25 to 30 km in Lahore district (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The two districts themselves were separated by an average of 70 km. The experimental areas were subjected to standard agricultural practices by the farmers. Throughout the study period, these areas were kept entirely free from insecticide applications.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eField management practices\u003c/h2\u003e \u003cp\u003eThe seedbed was prepared using one deep plow and followed by two cultivations using a tractor-mounted cultivator during both cropping seasons 2018 and 2019. The hybrid maize variety \u0026ldquo;Pioneer-P2848W\u0026rdquo; was chosen and the maize was sown between 14th and February 25th in both 2018 and 2019. Maize was manually sown with ridges spaced 75cm apart and with a plant-to-plant distance of 30cm. Standard N treatments were followed for nitrogen fertilizer application and 172 kg per hectare of phosphorous was applied. During the whole cycle of the maize crop, 12 irrigations (especially water-need-based stages like tasseling, silking, cob, and grain development) were applied through a manual tube well. Manual weed management was applied using hand picking and hoeing. No herbicide was applied at any experimental sites. Notably in 2018, the study sites in the Kasur district were characterized by a monoculture of maize, while in 2019, they were surrounded by Alfalfa. Conversely, in the Lahore district, the study sites were surrounded by Alfalfa in 2018 and maize monoculture in 2019.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eSampling and estimation of arthropod pests\u003c/h2\u003e \u003cp\u003ePests in maize fields can be found almost anywhere because many are ground dwellers, while others live in the lower regions of plants, the middle, and even the top of plants near the canopy. As a result, several methods such as sweep netting, visual counting, hand picking, beat-sheets, and pitfall trapping were utilized to capture arthropods during different phenological stages of the maize crop i.e., vegetative, tasseling, silking and maturity. The sampling methods differed in their capture efficiency in terms of species richness and family composition. All samples were collected twice a day on selected date i.e., early in the morning and late in the evening. Besides, the meteorological data (air temperature, rainfall and humidity), site information, and collector name were recorded for data analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003eSweep netting\u003c/h2\u003e \u003cp\u003eSweep net sampling was used to collect flying and mobile arthropod pests (aphids, thrips, leafhoppers, spider mites, whitefly, adults of armyworms, and stem borers) in maize crops, especially for canopy-dwelling arthropods. Each sweep involved gently swinging the net back and forth numerous times to remove insects from the plants. At each location within the sites, twenty-five hits (gauze net with 30cm diameter) were performed by walking through each plot between 10 am to 4 pm. The collected insects were carefully transported from the net into labeled containers and temporarily stored before further processing for identification. The collected pest fauna was counted to identify and calculated their abundances in maize experimental sites.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePitfall traps\u003c/h2\u003e \u003cp\u003ePitfall traps are the best known and most often used inventory method in agroecosystems (Ahmed et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Pitfall traps were installed for the monitoring of ground dwelling arthropods that are active near the soil surface especially for Carabidae, Saltatoria, and Elateridae families. For the ground-dwelling pest collection, 25 pitfalls (7-inch length and 3-inch width) were installed at each sub-site at 2.5 meters apart. The pitfall traps were inserted in the ground so that lip was flushed with the soil. One-third of each jar was filled with a mixture of ethyl acetate (70%) and water (30%). A few drops of the liquid detergent were also added in each pitfall to lower the surface tension. All pitfall traps were covered with transparent plexi-glass roof supported 5cm above the trap with four nails. During each sampling period, pitfall traps were emptied every two days by filtering out the pest specimens through a strainer. The collected pests were placed in vials with 75% ethanol.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eYellow pan traps\u003c/h2\u003e \u003cp\u003eFor the actively moving small pests like winged aphids, locally made yellow pan traps (15 \u0026times; 24 cm with 5 cm depth) were installed 15cm above the canopy. These pans were exposed for 24 hours to trap insect pests through yellow color attraction. Also, 4\u0026ndash;5 yellow sticky pans coated with the grease were used in each maize field plot.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003eBeating sheet\u003c/h2\u003e \u003cp\u003eThe beating sheet method was used to catch canopy inhabiting pests maize pests from field and surrounding margins. During this method, inverted white umbrella was placed beneath the maize plants and foliage was gently shaken. This technique was especially used during the tasseling and maturity growth stages of maize crops.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eVisual counting and hand-picking\u003c/h2\u003e \u003cp\u003eTo enhance the accuracy of arthropod pest catch, visual counting and hand-picking methods were also used. The visual counting approach was used to calculate the total number and relative abundance of several insect pests such as maize weevil, corn earworm, European corn borer etc. Twenty-five plants were selected from each corn field plot and studied using a Randomized Complete Block Design (RCBD). Counting was performed on the selected plants' leaves (upper, middle, and lower) using the naked eye or a magnifying glass (4x). Many active fliers escaped away quickly upon approach, thus many were computed visually.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSample preservation and morphological identification\u003c/h2\u003e \u003cp\u003eCollected specimens from each site were placed in 25 ml glass vials filled with a solution of 80% ethanol and a few drops of glycerin, depending on the size of the catches. Each vial was clearly labeled with the collection location, date, and such other important information. Initially, all collected specimens were frozen. Subsequently, they were transferred to the Agricultural Entomology and Toxicology Laboratory, Department of Zoology, Government College University, Lahore for further counting and identification. To remove field debris, the specimens were briefly rinsed with 75% alcohol before final preservation in 95% ethanol and storage in a refrigerator. The collected specimens were carefully identified to the lowest possible taxonomic level by examining their morphological characters under a stereo zoom microscope (IRMECO GmbH, model IM-SZ-500) equipped with a digital camera (Cannon Power Shot G9). The pest fauna was identified consulting available keys and catalogues such as Ortega (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1987\u003c/span\u003e); O'Day et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1998\u003c/span\u003e); Steffey et al. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1992\u003c/span\u003e); Kumar et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), and Ekman \u0026amp; Duff (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The immature pests were identified to the genus level. Additionally, arthropod pests\u0026rsquo; data available on the Barcode of Life Data Systems (BOLD) was also consulted to assist in identification. Voucher specimens were deposited in the Stephenson Natural History Museum, Government College University Lahore, Pakistan for further reference. Geographical coordinates, elevation, and other ecologically relevant data such as temperature (LM-8000), humidity (R6001 Thermo-Hygrometer), and rainfall were collected using a portable GPS device (Garmin model 010-02256-00) and an environmental data recorder.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eBiodiversity and similarity measures\u003c/h2\u003e \u003cp\u003eTo estimate the total species richness of pests\u0026rsquo; fauna in both districts, two of the most widely used estimators, Chao 1 and Chao 2, were computed using the Estimate S program. Chao 1 is considered a minimum estimator of species richness, particularly effective when the number of species represented by single-tones and double-tones species is high (Chao et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({S_1}={S_{obs}}+\\frac{{{F_1}^{2}}}{{2{F_2}}}\\)\u003c/span\u003e \u003c/span\u003e (1) Where: \u003cem\u003eS\u003c/em\u003e\u003csub\u003e\u003cem\u003eobs\u003c/em\u003e\u003c/sub\u003e was the number of species in the sample, \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e the number of single tone species in the sample and \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e is the number of double tone species in the sample.\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$${S_2}={S_{obs}}+\\frac{{{Q_1}^{2}}}{{2{Q_2}}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere: \u003cem\u003eS\u003c/em\u003e\u003csub\u003e\u003cem\u003eobs\u003c/em\u003e\u003c/sub\u003e was the number of species in the sample, \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e the number of single tone species in the sample and \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e is the number of double tone species in the sample.\u003c/p\u003e \u003cp\u003eThe diversity of pests at different selected sites was analyzed using two widely used diversity indices: the Shannon-Wiener index, which is sensitive to change in the abundance of rare species within the community, and the Simpson index, which is more sensitive to the most abundant species (Morris et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The Shannon-Wiener index (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(H\u0026#039;\\)\u003c/span\u003e\u003c/span\u003e) was calculated using the following formula:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$H\u0026#039;= - \\sum {{P_i}\\log {p_i}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({p_i}=\\frac{n}{N}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eSpecies richness was also computed using Margalef Index, based on the relationship between species richness (S) and total number of individuals observed (N).\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$d\u0026#039;=\\frac{{S - 1}}{{\\ln N}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eMenhinick index was used to assess the relationship between the number of species present in the sample and the total number of individuals collected. Evenness indices, described how evenly the species are distributed in the sample. A high evenness index value indicates that all species in the sample are equally distributed. Conversely, a decreasing evenness value towards zero indicates that the relative abundance of species diverged away from evenness. The modified Hill\u0026rsquo;s Ratio (E-5) is the most reliable evenness index as it is independent of number of the species in the sample.\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$E5=(1/D) - 1/{e^{H\u0026#039;}} - 1$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere: D\u0026thinsp;=\u0026thinsp;Simpson's index, and H\u0026thinsp;=\u0026thinsp;Shannon-Wiener index\u003c/p\u003e \u003cp\u003eAll the diversity indices were computed using the statistical software SPDIVERS.BAS. The degree of the association of the sampling sites was found using cluster analysis. It is a useful data reduction technique that can be helpful in the analysis of grouping of the objects. The cluster analysis was performed through MSVP. Ver. Similarity estimates were analyzed using the unweight pair group method with arithmetic mean (UPGMA) and the resulting clusters were represented as dendrograms. The daily rainfall and temperature data were recorded in the selected sampling locations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eBefore further analysis, obtained data in this study was assessed for normality using the Shapiro-Wilk test. Since non-significant difference (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) was found in arthropod pest populations at selected locations during both cropping seasons (2018\u0026ndash;2019), the data was combined for further analysis. Species accumulation curves were generated using the SPDIVERS.BAS program to estimate whether the sampling effort was sufficient to estimate the total number of species (Ludwig and Reynolds \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). Additionally, a logarithmic tendency curve was generated to visualize the increase in the number of species. As the biodiversity data collection is labor-intensive and time-consuming, a small portion of the community is represented by rare species, often which are mostly single tones, might remain undetected by most surveys. Spearman's rank correlation coefficient was used to analyze the correlation among species abundance in all fields and meteorological parameters (temperature and relative humidity).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eArthropod pests\u0026rsquo; communities in maize plots were investigated throughout the 2018 and 2019 cropping seasons, and the cumulative number of arthropod pests was analyzed. A total of 97671 insect pests representing 49 species belonging to 45 genera, 27 families, and 6 orders were recorded from both Kasur and Lahore districts. Identification of the captured insects revealed that 32525 individuals were immature, and their identification was limited to the genus level due to the unavailability of suitable keys for juvenile stages. The remaining 65175 mature individuals were classified to the species level (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Generally, family Noctuidae was the most abundant one among the major pests\u0026rsquo; families affecting maize crops (49.17%), followed by Crambidae (14.94%), Thripidae (7.59%), Aphididae (6.28%), Cicadellidae (4.25%), Acrididae (3.30%), Muscidae (2.46%), Delphacidae (2.16%), Curculionidae (1.75%), Lygaeidae (1.09%), Pentatomidae (1.02%), and the last was Chrysomelidae (0.94%). The combined proportion of all remaining families was less than 4.27% of the total catch. The fall armyworm, \u003cem\u003eSpodoptera frugiperda\u003c/em\u003e (J.E. Smith, 1797) (Family: Noctuidae) was the most dominant species; constituting (18.51%) of the total pests\u0026rsquo; catches, followed by \u003cem\u003eChilo partellus\u003c/em\u003e (Swinhoe), (14.94%), and \u003cem\u003eHelicoverpa armigera\u003c/em\u003e (H\u0026uuml;bner), (13.84%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe relative abundance (%) of pests associated with maize crops of two districts of the Punjab, Pakistan.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrder\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFamily\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKasur\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLahore\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eR.A. %\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eColeoptera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChrysomelidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eMonolepta signata\u003c/em\u003e (Olivier, 1808 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eChaetocnema pulicaria\u003c/em\u003e (Melsheimer)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eDiabrotica virgifera virgifera\u003c/em\u003e LeConte, 1868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eOulema\u003c/em\u003e sp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoccinellidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eEpilachna varivestis\u003c/em\u003e (Mulsant, 1850)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCurculionidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eMyllocerus undecimpustulatus\u003c/em\u003e (Faust, J. 1891)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSitophilus zeamais\u003c/em\u003e (Motschulsky, 1855)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eAnthonomus grandis\u003c/em\u003e (Boheman, 1843)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eMyllocerus\u003c/em\u003e sp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElateridae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eAgriotes\u003c/em\u003e sp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScarabaeidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eOxygrylius ruginasus\u003c/em\u003e (Le Conte, 1856)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eDigitonthophagus gazelle\u003c/em\u003e (Fabricius, 1787)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePhyllophaga\u003c/em\u003e sp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTenebrionidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eTenebrionidae\u003c/em\u003e sp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMeloidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eMylabris pustulata\u003c/em\u003e (Thunberg, 1821)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eMeloidae\u003c/em\u003e sp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiptera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMuscidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eAtherigona soccata\u003c/em\u003e Rondani\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eAtherigona orientalis\u003c/em\u003e\u0026nbsp;(Schiner)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemiptera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAphididae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eRhopalosiphum maidis\u003c/em\u003e (Fitch, 1856)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6,133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCicadellidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCicadulina mbila\u003c/em\u003e (Naud\u0026eacute;,1924)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4,152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDelphacidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePeregrinus maidis\u003c/em\u003e (Ashmead, 1890)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLophopidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePyrilla perpusilla\u003c/em\u003e (Walker, 1851)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLygaeidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eOxycarenus laetus\u003c/em\u003e (Kirby, 1891)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSpilostethus saxatilis\u003c/em\u003e (Scopoli, 1763)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoreidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCletus pugnator\u003c/em\u003e (Fabricius, 1787)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFamily\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eSpecies\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eKasur\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eLahore\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eR.A. %\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePentatomidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eEysarcoris ventralis\u003c/em\u003e (Westwood, 1837)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eNezara viridula\u003c/em\u003e (Linnaeus, 1758)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eBagrada hilaris\u003c/em\u003e (Burmeister, 1835)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudococcidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eHeterococcus nudus\u003c/em\u003e (Green, 1926)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePyrrhocoridae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eDysdercus cingulatus\u003c/em\u003e (Fabricius, 1775)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLepidoptera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrambidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eChilo partellus\u003c/em\u003e (Swinhoe)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14,593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eErebidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eLaelia suffusa\u003c/em\u003e (Hampson, 1893)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGeometridae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eGeometridae\u003c/em\u003e sp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHesperiidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePelopidas mathias\u003c/em\u003e (Fabricius, 1798)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNoctuidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eAgrotis ipsilon\u003c/em\u003e (Hufnagel)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eBusseola fusca\u003c/em\u003e (Fuller, 1901)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6,493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eHelicoverpa zea\u003c/em\u003e (Boddie)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9,501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eHelicoverpa armigera\u003c/em\u003e (H\u0026uuml;bner)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13,523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSpodoptera frugiperda\u003c/em\u003e (J.E. Smith, 1797)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9,875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8,214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18,089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNolidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eEarias insulana\u003c/em\u003e (Boisduval, 1833)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eEarias vittella\u003c/em\u003e (Fabricius, 1794)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrthoptera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcrididae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eAcrida willemsei\u003c/em\u003e (Dirsh, 1954)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eHieroglyphus perpolita\u003c/em\u003e (Uvarov, 1933)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePyrgomorphidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eChrotogonus trachypterus\u003c/em\u003e (Blanchard, 1836)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTettigoniidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eOxyiachinensis\u003c/em\u003e sp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThysanoptera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThripidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eThrips tabaci\u003c/em\u003e (Lindeman, 1889)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eFrankliniella williamsi\u003c/em\u003e Hood, 1915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4,937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e49297\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e48403\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e97700\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe analysis of species accumulation curves (pooled for two years data) for insect pests in the two districts is shown (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The number of trappable insect pest species increased continuously with the increase of sample size. The curve initially had a steep slope, indicating a rapid increase in the number of species as more individuals were sampled. However, the curve did not reach an asymptote. The Chao 1 and Chao 2 estimator were used to provide a more accurate assessment of the total species diversity in each area. Based on the Chao-2 estimate, the estimated species richness was 45.72 and 47.42 at Kasur and Lahore districts, respectively. This suggests that the ratio of observed to estimate the species number was 95% and 97% for districts Kasur and Lahore, respectively. According to the species completeness data, at least 5% and 3% of species in Kasur and Lahore, respectively, are expected present in the study area but were not captured during this sampling (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSpecies diversity and inventory completeness for insect pests collected from Kasur and Lahore districts.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistrict Kasur\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDistrict Lahore\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of specimens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48403\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObserved richness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo of singletons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo of Doubletons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEstimated Species Richness\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChao 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChao 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% completeness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eArthropod pest biodiversity was analyzed using four different indices. District Kasur had the total pest capture (49297) compared to Lahore (48403). Pest richness as measured by the Menhinick and Margalef indices did not significantly differ between both districts. Similarly, diversity indices, including the Shannon-Wiener Index and Simpson's Index, showed non-significant difference between Kasur and Lahore (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The modified Hill ratio (E-5), which reflects the dominance of specific pest species, displayed almost uniform values for both districts, suggesting a relatively similar degree of dominance in pest species between the two regions. A cluster analysis based on the similarity in pest species composition showed clear groupings of the study sites in each district. Sites K1 and K2 from Kasur formed a distinct clusters with 93% similarity in pest species, while sites L2 and L3 in Lahore showed another cluster with 89% similarity. However, site K3 displayed 92% similarity with other Kasur sites (K1-K2), and site L1 from Lahore displayed 87% similarity with the other sites (L2-L3) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTotal abundance, richness, diversity, and evenness indices for the insect pests collected from Kasur and Lahore districts in 2018 and 2019 seasons.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eStudy areas\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eDistrict Kasur\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003eDistrict Lahore\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of Specimens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e49297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e48403\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRichness Indices\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMargalef Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e4.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e4.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMenhinick Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e0.203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eDiversity Indices\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShannon-Wiener Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e3.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e3.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSimpson's Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvenness index (E-5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e0.903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.904\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePopulation dynamics of pest species in Kasur and Lahore districts during 2018 and 2019 are presented in (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Both districts exhibited similar population fluctuation pattern. Generally, the density of pest populations showed uni-modal seasonal pattern, with peak activity in April. However, the lowest densities were in June and July for both Kasur and Lahore districts during both cropping seasons (2018\u0026ndash;2019). There was a strong positive correlation between the population densities of fall armyworm, with temperature (r\u0026thinsp;=\u0026thinsp;0.750, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), whereas maize aphid population densities showed negative correlation with temperature (r = -0.714, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and humidity (r = -0.367, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Maize stem borer showed weaker correlations with temperature (r\u0026thinsp;=\u0026thinsp;0.429, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) and humidity (r = -0.297, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Furthermore, maize earworm showed significant positive correlation with temperature (r\u0026thinsp;=\u0026thinsp;0.690, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and negative correlation with humidity (r = -0.468, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) and rainfall (r= -0.359, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e illustrated the major pests of maize crops from the study districts.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe association of the major pest species with temperature, rainfall and humidity during maize growing seasons (2018\u0026ndash;2019).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMajor Pest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTemperature\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRainfall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHumidity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFall armyworm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er = 0.750*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003er = -0.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er = -0.279\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.509\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStem borer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003er = -0.297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er= -0.286\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.487\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMaize earworm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003er = -0.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er= -0.359\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep= -0.495\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e* Correlation is significant at the 0.05 level\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe study provides baseline data on major insect pests of maize crops of Punjab, Pakistan. Maize, grown year-round is susceptible to various insect pests from seedling to harvest (Khan et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The diversity, seasonal abundance, and population dynamics of these pests vary monthly. Over a two year survey in two major maize growing districts, 97671 arthropod pests from 49 species were recorded. The family Noctuidae was the most dominant at all six sampling sites, constituting nearly 50% of the total pest catch. Within this family, \u003cem\u003eS. frugiperda\u003c/em\u003e was the most prevalent pest (18.51%); followed by \u003cem\u003eChilo partellus\u003c/em\u003e (14.94%), \u003cem\u003eHelicoverpa armigera\u003c/em\u003e (13.84%) and \u003cem\u003eH. zea\u003c/em\u003e (Boddie) (9.72%). The current findings align with Urge et al. (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), who reported similar dominant pests in Ethiopian maize fields. Maize thrips (\u003cem\u003eFrankliniella williamsi\u003c/em\u003e), maize aphids \u003cem\u003eRhopalosiphum maidis\u003c/em\u003e, \u003cem\u003eAtherigona soccata\u003c/em\u003e, maize leafhopper (\u003cem\u003eCicadulina mbila)\u003c/em\u003e and \u003cem\u003eChaetocnema pulicaria\u003c/em\u003e constituted the sucking pest complex; targeting maize vegetative stages by extracting the plant sap (Paul et al. 2020). Furthermore, FAW; corn earworm (\u003cem\u003eH. armigera\u003c/em\u003e \u0026amp; \u003cem\u003eH. zea)\u003c/em\u003e and corn root worms (\u003cem\u003eAgrotis ipsilon\u003c/em\u003e collectively form the chewing pest complex. They target the maize at different phenological stages by chewing different parts or by transmitting different types of bacteria and viruses (Soujanya et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The sucking pests like leaf hopper, \u003cem\u003eC. mbila\u003c/em\u003e usually attack the early vegetative and tasseling stages of maize. Maize aphids are widely prevalent pests; sucking the juice of the soft vegetative parts throws their mouthparts and serves as vector for the virus transmission (Oberemok et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNotably, FAW is a major invasive pest species of maize globally, infesting all stages of maize crops (Dassou et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In our study, it was recorded as the dominant pest, comprising 18.51% of the total pest catch. FAW, a polyphagous insect also attacks other economic cash crops like rice, wheat, cotton, and sorghum (Mlambo et al. 2023). It causes a severe damage to the vegetative and reproductive parts of maize plants. Initially, young FAW larvae feed near the ground level. As they mature, the larvae start creating holes in leaves or stems from the outside inward. Mature leaves may have three to four rows of tiny holes (Day et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Larval densities ranging from 0.2\u0026ndash;0.8% per plant at the late whorl stage can result in 5\u0026ndash;20% yield losses (Makgoba et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). FAW larvae hide within the maize funnel during the day and feed at night (Day et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The presence of FAW on maize crops in Pakistan has been reported by other researchers. Gilal et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) reported 100% damage to fodder maize in Sindh district. Similarly, Ibrahim et al. (2022) found FAW in maize fields in Kasur and Lahore districts, with peak infection rates reaching 19.39%. Notably, damage was more severe at the edges of the maize fields compared to the central regions. This could be due to the probable migration of FAW from neighboring crops such as wheat and rice.\u003c/p\u003e \u003cp\u003eIn addition to FAW, the study reported numerous other notable insect pests of maize crops in Pakistan. Maize steam borer, \u003cem\u003eChilo partellus\u003c/em\u003e from the family Crambidae was ranked as the second most abundant pest; constituting 14.94% of the total pest catches. This pest is globally widespread in maize; causing yield losses up to 42.29% (Guo et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The larvae are more destructive, tunneling in the stem or stalk after hatching from eggs and hindering ear formation. They can move between plants through the holes made in the lower stem nodes (Srivastava et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Nabeel et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) also identified this species as the main maize pest in Punjab, Pakistan. The Noctuid moths of \u003cem\u003eHelicoverpa armigera\u003c/em\u003e (H\u0026uuml;bner) (13.84%) and \u003cem\u003eH. zea\u003c/em\u003e (Boddie) (9.72%) also caused significant losses in maize crops. These results are agreeable with those reported previously by Wang et al. (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) in China. Moreover, Keszthelyi et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) reported that \u003cem\u003eH. armigera\u003c/em\u003e can reduce average maize ear weight by 13.99%. It is also considered as key pest in agriculture and horticulture in Pakistan. On the other hand, \u003cem\u003eH. zea\u003c/em\u003e is a polyphagous insect pest feeds on various crops especially maize. The larvae can damage both cultivated and wild host plants, particularly when feeding the maize ear. Their larvae initially feed on the silk and then move down to the ear tip. Previous studies by Sarwar (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) confirmed the presence of \u003cem\u003eH. zea\u003c/em\u003e in maize fields of Pakistan.\u003c/p\u003e \u003cp\u003eThis study documented the presence of other pest species besides the dominant ones discussed earlier. These included maize aphids, grasshoppers, shoot flies, and white grubs. In this study, 96% of the species were successfully captured in both districts during the study period (2018\u0026thinsp;\u0026minus;\u0026thinsp;209). The remaining 4% of targeted crop pests might represent rare species, or their activity patterns or timing might have differed from the sampling schedule, leading to their undetected presence. This is evident in from species accumulation curves, which did not reach an asymptote at both districts. Similar findings were reported by Schmidt and Balakrishnan (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), suggesting that various insect species may exhibit varying activity times to avoid competition. It is also possible that the methods for collecting insect pest samples weren't sufficient enough to guarantee a 100% complete pest inventory in both research regions. Furthermore, some pest species might only emerge sporadically during cropping seasons, potentially escaping capture during the sample phase. This is consistent with observations by other entomologists studying on the agricultural crops biodiversity. For example, Borges and Brown (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) reported 90% completeness in their species inventory of arthropods in Azorean pasture, while Nadeem et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) recorded 94% of pest species from the cotton crops in Pakistan. This study revealed a distinct seasonal pattern in pest abundance during the cropping years (2018\u0026ndash;2019). The number of insect pests began to increase in mid-March, reaching recorded peak in April. These findings align with the previous research conducted by Khan et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) in maize fields in Khyber Pakhtunkhwa, Pakistan. This data not only confirm the presence of predictable seasonal trends in pest populations, but also highlight the resilience of such patterns across various agricultural contexts in the Pakistan. The observed seasonal dynamics and minor changes in diversity indices are most likely caused by a combination of meteorological variables, crop phenology, and agricultural methods.\u003c/p\u003e \u003cp\u003eGiven how temperature, humidity, and precipitation affect insect communities, it is possible that these climatic factors contributed to the very uniform diversity measurements observed (Vasconcellos et al. 2019). Furthermore, in addition to climatic conditions, other ecological factors, such as soil composition, vegetation structure, and land management methods, may have influenced local insect diversity patterns (Duan et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The difference between the actual and estimated species richness using Chao-1 and Chao-2 estimator suggests that the sampling methods used to capture insect pests were not enough. It suggests that more intensive sampling efforts needed with additional sampling techniques for better capture of insect pests at both districts. Furthermore, extending the sampling time could also be an option for better pest capture. This approach may assist to capture a wider range of insect pests with diverse activity patterns throughout the day (Montgomery et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The cluster analysis dendrogram revealed a clear grouping of sampling sites in both districts. This suggests that the pest communities share a similar geographic and environmental range. This observation is further supported by the minimal differences observed in maize field data collected from different sites within each district.\u003c/p\u003e \u003cp\u003eClimatic factors significantly impact the diversity of maize pests, influencing their population dynamics, distribution, and interaction with the natural predators (spiders, coccinellids, green lacewing) (Thomson et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In the present research, FAW showed positive correlation (r\u0026thinsp;=\u0026thinsp;0.750; p\u0026thinsp;=\u0026thinsp;0.024) with the temperature consistent with the findings of Soumia et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This might be due to enhanced reproductive rate and development of FAW in warmer conditions. Furthermore, the research revealed that maize stem borer (\u003cem\u003eChilo partellus\u003c/em\u003e) populations exhibited a positive correlation with temperature (r\u0026thinsp;=\u0026thinsp;0.429; p\u0026thinsp;=\u0026thinsp;0.274) while showing negative correlation with the rainfall (r = -0.106; p\u0026thinsp;=\u0026thinsp;0.788) and humidity (r = -0.297; p\u0026thinsp;=\u0026thinsp;0.481) levels. These finding suggest that higher temperatures favor proliferation of stem borer, whereas increase humidity and rainfall potentially due to the disruption of their habitat and life cycle. The results are supported by global research that has shown comparable trends and correlations between temperature, rainfall, humidity, and the abundance of maize stem borer populations (R\u0026eacute;gnier et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In contrast to the above trends, maize aphids showed a negative association with temperature and a positive correlation with humidity levels. It is worth noting that the maize aphids observed in the current study were primarily found during the early vegetative phases (March and April) of maize crops. This could be because maize crops' tender stems and leaves produce more sap than mature crop stages (Șimon et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In general, sucking pests dominated in April and May, while chewing pests were more prevalent from May to June (Murtaza et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe study highlights the vulnerability of maize crop to different insect pest complexes throughout its growing season. Sucking pests attack the early vegetative stages of maize. As the plant proceeds to the next growth stage, chewing pests particularly fall armyworm and maize stem borers attack the vegetative plants. Attributing pest population fluctuations solely to environmental factors can be challenging. Future research could expand on the current study's findings to investigate arthropod diversity for more effective domestic and internationally Integrated Pest Management strategies.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledges\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors express their gratitude to ORIC, Government College University, Lahore, Pakistan for supporting this project. We also deeply appreciate the farmers in Kasur and Lahore districts for their invaluable assistance with our research.\u0026nbsp;Additionally, we wish to acknowledge the referees for their thorough evaluation and constructive feedback, which greatly aided in the revision of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no competing interests to declare that are relevant to the content of this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbdullah MH, Ahmad A, Saboor A, Aftab M, Baig IA, Iftikhar M, Hussain J (2022) Climatic variability during cropping seasons in agroecological zones of Pakistan. 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Int J Food Prop 22(1):572582. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/10942912.2019.1596124\u003c/span\u003e\u003cspan address=\"10.1080/10942912.2019.1596124\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-tropical-insect-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtis","sideBox":"Learn more about [International Journal of Tropical Insect Science](http://link.springer.com/journal/42690)","snPcode":"42690","submissionUrl":"https://www.editorialmanager.com/jtis/default2.aspx","title":"International Journal of Tropical Insect Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Noctuidae, fall armyworm, diversity indices, species richness, pest management, maize crops","lastPublishedDoi":"10.21203/rs.3.rs-4301820/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4301820/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMajor gaps exist regarding the biodiversity and population ecology of insect pests in maize crop in Pakistan. The objective of this study was to survey the species composition, relative abundance, and population dynamics of arthropod insect pests in maize crop in Punjab, Pakistan. A two-year (2018\u0026ndash;2019) survey of insect pests\u0026rsquo; species biodiversity in field maize crops was carried out in two districts (Kasur and Lahore). A total of 49 pest species belonging to 45 genera, 27 families, and 6 orders were recorded in this study. Noctuidae dominated over the other pest families, constituting 49.17% of the total pests catch. Fall armyworm, \u003cem\u003eSpodoptera frugiperda\u003c/em\u003e (J.E. Smith) was found to be the most dominant species, constituting 18.51% of the sampled individuals. Moreover, the estimated pest species richness from both districts was 94%. While, the diversity indices (Shannon-Weiner and Simpson) revealed non-significant differences in arthropod pest communities at six selected sites. Using the Menhinick and Margalef indices suggested higher species richness in the Lahore district. Overall, the pests population densities were consistently fluctuated throughout both cropping seasons; peaking in April-May and reaching the lowest levels in June-July. Spearman's rank correlation analysis indicated a negative association between insect abundance and temperature while, non-significant correlation was found with humidity in both districts. These findings can help to develop sustainable pests\u0026rsquo; control strategies, with implications both at local and global scale in maize growing areas.\u003c/p\u003e","manuscriptTitle":"Survey and seasonal abundance of major insect pests in the maize fields of Punjab, Pakistan","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-25 12:57:30","doi":"10.21203/rs.3.rs-4301820/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-11-17T04:05:13+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-11T12:53:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-30T09:42:49+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"International Journal of Tropical Insect Science","date":"2024-07-29T02:49:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Tropical Insect Science","date":"2024-07-26T09:41:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-tropical-insect-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtis","sideBox":"Learn more about [International Journal of Tropical Insect Science](http://link.springer.com/journal/42690)","snPcode":"42690","submissionUrl":"https://www.editorialmanager.com/jtis/default2.aspx","title":"International Journal of Tropical Insect Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"13ed75b3-2e5d-4d76-962d-1be5837467ed","owner":[],"postedDate":"July 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-09-01T16:03:46+00:00","versionOfRecord":{"articleIdentity":"rs-4301820","link":"https://doi.org/10.1007/s42690-025-01588-3","journal":{"identity":"international-journal-of-tropical-insect-science","isVorOnly":false,"title":"International Journal of Tropical Insect Science"},"publishedOn":"2025-08-28 15:57:15","publishedOnDateReadable":"August 28th, 2025"},"versionCreatedAt":"2024-07-25 12:57:30","video":"","vorDoi":"10.1007/s42690-025-01588-3","vorDoiUrl":"https://doi.org/10.1007/s42690-025-01588-3","workflowStages":[]},"version":"v1","identity":"rs-4301820","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4301820","identity":"rs-4301820","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}