O'Neal,
Matthew
O'Neal,
Matthew
Email Address
oneal@iastate.edu
Birth Date
Title
Professor
Academic or Administrative Unit
Organizational Unit
Department of Entomology
The Department of Entomology seeks to teach the study of insects, their life-cycles, and the practicalities in dealing with them, for use in the fields of business, industry, education, and public health. The study of entomology can be applied towards evolution and ecological sciences, and insects’ relationships with other organisms & humans, or towards an agricultural or horticultural focus, focusing more on pest-control and management.
History
The Department of Entomology was founded in 1975 as a result of the division of the Department of Zoology and Entomology.
Related Units
- College of Agriculture and Life Sciences (parent college)
- Department of Zoology and Entomology (predecessor, 1975)
Organizational Unit
Plant Pathology, Entomology and Microbiology
The Department of Plant Pathology and Microbiology and the Department of Entomology officially merged as of September 1, 2022. The new department is known as the Department of Plant Pathology, Entomology, and Microbiology (PPEM). The overall mission of the Department is to benefit society through research, teaching, and extension activities that improve pest management and prevent disease. Collectively, the Department consists of about 100 faculty, staff, and students who are engaged in research, teaching, and extension activities that are central to the mission of the College of Agriculture and Life Sciences. The Department possesses state-of-the-art research and teaching facilities in the Advanced Research and Teaching Building and in Science II. In addition, research and extension activities are performed off-campus at the Field Extension Education Laboratory, the Horticulture Station, the Agriculture Engineering/Agronomy Farm, and several Research and Demonstration Farms located around the state. Furthermore, the Department houses the Plant and Insect Diagnostic Clinic, the Iowa Soybean Research Center, the Insect Zoo, and BugGuide. Several USDA-ARS scientists are also affiliated with the Department.
About
Profile Link
ORCID iD
Publications
Now showing
1 - 10 of 222
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Article
Are honey bees altering wild plant–bee interactions in reconstructed native habitats? An investigation of summer season effects in row-crop agroecosystems with prairie strips
2025-04-23
,
Borchardt, Kate E.
,
Moore, Morgan
,
Cass, Randall
,
O'Neal, Matthew
,
Toth, Amy
,
Department of Ecology, Evolution, and Organismal Biology (CALS)
,
Plant Pathology, Entomology and Microbiology
,
Pollinator Working Group
1. Including native habitats in the margins of an intensifying agricultural environment may help conserve organisms such as bees, which can also utilise crop species for sustenance. Nearly 25% of wild bee species in the United States are in danger of extinction and finding floral resources for managed honey bees (Apis mellifera [L.], Apidae) is becoming increasingly difficult. Therefore, both beekeepers and wild bee populations are increasingly reliant on the shrinking native habitat in
agroecosystems.
2. We investigated the compatibility of beekeeping with pollinator conservation in one conservation practice known as ‘prairie strips’ integrated into agricultural landscapes. Prairie strips are native plant communities planted within crop fields that provide agronomic benefits while conserving native organisms. We analysed plant–bee interactions and bumble bee body condition at row-crop fields integrated with prairie strips with and without the presence of a commercial-sized apiary of 20 honey bee colonies, during the summer season (June to August) in 2021.
3. We found no effect of apiaries on the abundance and richness of wild bees or bumble bees and no difference in plant–pollinator network structure. Bombus bimaculatus [Cresson, Apidae] had a lower dry mass at prairie strips with apiaries than at prairie strips without. However, there was no difference in dry mass in the other two bumble bee species and no difference in all three bumble bee species when we analysed body size and average wing area.
4. Our study suggests commercial-sized apiaries may have little effect on ecosystem function, wild bee communities and bumble bee body condition from June to August. However, this study did not address the effects of honey apiaries across seasons and years. More research is needed to determine if a commercial-sized apiary would affect wild bee communities after August when honey bees begin visiting native prairie plants more frequently.
2. We investigated the compatibility of beekeeping with pollinator conservation in one conservation practice known as ‘prairie strips’ integrated into agricultural landscapes. Prairie strips are native plant communities planted within crop fields that provide agronomic benefits while conserving native organisms. We analysed plant–bee interactions and bumble bee body condition at row-crop fields integrated with prairie strips with and without the presence of a commercial-sized apiary of 20 honey bee colonies, during the summer season (June to August) in 2021.
3. We found no effect of apiaries on the abundance and richness of wild bees or bumble bees and no difference in plant–pollinator network structure. Bombus bimaculatus [Cresson, Apidae] had a lower dry mass at prairie strips with apiaries than at prairie strips without. However, there was no difference in dry mass in the other two bumble bee species and no difference in all three bumble bee species when we analysed body size and average wing area.
4. Our study suggests commercial-sized apiaries may have little effect on ecosystem function, wild bee communities and bumble bee body condition from June to August. However, this study did not address the effects of honey apiaries across seasons and years. More research is needed to determine if a commercial-sized apiary would affect wild bee communities after August when honey bees begin visiting native prairie plants more frequently.
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Self-supervised learning improves classification of agriculturally important insect pests in plants
2023-07-18
,
Kar, Soumyashree
,
Nagasubramanian, Koushik
,
Elango, Dinakaran
,
Carroll, Matthew E.
,
Abel, Craig A.
,
Nair, Ajay
,
Mueller, Daren S.
,
O'Neal, Matthew
,
Singh, Asheesh
,
Sarkar, Soumik
,
Ganapathysubramanian, Baskar
,
Singh, Arti
,
Department of Agronomy
,
Mechanical Engineering
,
Department of Horticulture
,
Plant Pathology, Entomology and Microbiology
Insect pests cause significant damage to food production, so early detection and efficient mitigation strategies are crucial. There is a continual shift toward machine learning (ML)-based approaches for automating agricultural pest detection. Although supervised learning has achieved remarkable progress in this regard, it is impeded by the need for significant expert involvement in labeling the data used for model training. This makes real-world applications tedious and oftentimes infeasible. Recently, self-supervised learning (SSL) approaches have provided a viable alternative to training ML models with minimal annotations. Here, we present an SSL approach to classify 22 insect pests. The framework was assessed on raw and segmented field-captured images using three different SSL methods, Nearest Neighbor Contrastive Learning of Visual Representations (NNCLR), Bootstrap Your Own Latent, and Barlow Twins. SSL pre-training was done on ResNet-18 and ResNet-50 models using all three SSL methods on the original RGB images and foreground segmented images. The performance of SSL pre-training methods was evaluated using linear probing of SSL representations and end-to-end fine-tuning approaches. The SSL-pre-trained convolutional neural network models were able to perform annotation-efficient classification. NNCLR was the best performing SSL method for both linear and full model fine-tuning. With just 5% annotated images, transfer learning with ImageNet initialization obtained 74% accuracy, whereas NNCLR achieved an improved classification accuracy of 79% for end-to-end fine-tuning. Models created using SSL pre-training consistently performed better, especially under very low annotation, and were robust to object class imbalances. These approaches help overcome annotation bottlenecks and are resource efficient.
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Evidence of enhanced reproductive performance and lack-of-fitness costs among soybean aphids, Aphis glycines, with varying levels of pyrethroid resistance
2022-02-01
,
Hodgson, Erin
,
O'Neal, Matthew
,
Valmorbida, Ivair
,
Coates, Brad S.
,
Hodgson, Erin W.
,
Ryan, Molly
,
O’Neal, Matthew E.
,
Department of Entomology
BACKGROUND: Foliar application of insecticides is the main strategy to manage soybean aphid, Aphis glycines (Hemiptera: Aphididae), in the northcentral United States. Subpopulations of A. glycines have multiple nonsynonymous mutations in the voltage-gated sodium channel (vgsc) genes that are associated with pyrethroid resistance. We explored if fitness costs are associated with phenotypes conferred by vgsc mutations using life table analyses. We predicted that there would be significant differences between pyrethroid susceptibility and field-collected, parthenogenetic isofemale clones with differing, nonsynonymous mutations in vgsc genes.
RESULTS: Estimated resistance ratios for the pyrethroid-resistant clones ranged from 3.1 to 37.58 and 5.6 to 53.91 for lambdacyhalothrin and bifenthrin, respectively. Although life table analyses revealed some biological and demographic parameters to be significantly different among the clonal lines, there was no association between levels of pyrethroid resistance and a decline in fitness. By contrast, one of themost resistant clonal lines (SBA-MN1-2017) had a significantly higher finite rate of increase, intrinsic rate of increase and greater overall fitness compared to the susceptible control and other pyrethroid-resistant clonal lines.
CONCLUSIONS: Our life history analysis suggests that there are no negative pleotropic effects associated with the pyrethroid resistance in the clonal A. glycines lines used in this study. We discuss the potential impact of these results on efficacies of insecticide resistance management (IRM) and integrated pest management (IPM) plans directed at delaying the spread of
pyrethroid-resistant A. glycines.
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Can Native Plants Mitigate Climate-related Forage Dearth for Honey Bees (Hymenoptera: Apidae)?
2021-11-25
,
Zhang, Ge
,
St. Clair, Ashley L.
,
Dolezal, Adam G.
,
Toth, Amy L.
,
O'Neal, Matthew E.
,
Department of Entomology
,
Department of Ecology, Evolution, and Organismal Biology (LAS)
,
Pollinator Working Group
Extreme weather events, like high temperatures and droughts, are predicted to become common with climate change, and may negatively impact plant growth. How honey bees (Apis mellifera L. [Hymenoptera: Apidae]) will respond to this challenge is unclear, especially when collecting pollen, their primary source of protein, lipids, and micro-nutrients. We explored this response with a data set from multiple research projects that measured pollen collected by honey bees during 2015–2017 in which above-average temperatures and a drought occurred in 2017. We summarized the abundance and diversity of pollen collected from July to September in replicated apiaries kept at commercial soybean and corn farms in Iowa, in the Midwestern USA. The most commonly collected pollen was from clover (Trifolium spp. [Fabales: Fabaceae]), which dramatically declined in absolute and relative abundance in July 2017 during a period of high temperatures and drought. Due to an apparent lack of clover, honey bees switched to the more drought-tolerant native species (e.g., Chamaecrista fasciculata [Michx.] Greene [Fabales: Fabaceae], Dalea purpurea Vent. [Fabales: Fabaceae], Solidago spp. [Asterales: Asteraceae]), and several species of Asteraceae. This was especially noticeable in August 2017 when C. fasciculata dominated (87%) and clover disappeared from bee-collected pollen. We discuss the potential implications of climate-induced forage dearth on honey bee nutritional health. We also compare these results to a growing body of literature on the use of native, perennial flowering plants found in Midwestern prairies for the conservation of beneficial insects. We discuss the potential for drought resistant-native plants to potentially promote resilience to climate change for the non-native, managed honey bee colonies in the United States.
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InsectNet: Real-time identification of insects using an end-to-end machine learning pipeline
2025-01
,
Chiranjeevi, Shivani
,
Saadati, Mojdeh
,
Deng, Zi K.
,
Koushik, Jayanth
,
Jubery, Talukder Z.
,
Mueller, Daren S.
,
O'Neal, Matthew
,
Merchant, Nirav
,
Singh, Aarti
,
Singh, Asheesh
,
Sarkar, Soumik
,
Singh, Arti
,
Ganapathysubramanian, Baskar
,
Mechanical Engineering
,
Department of Computer Science
,
Plant Pathology, Entomology and Microbiology
,
Department of Agronomy
Insect pests significantly impact global agricultural productivity and crop quality. Effective integrated pest management strategies require the identification of insects, including beneficial and harmful insects. Automated identification of insects under real-world conditions presents several challenges, including the need to handle intraspecies dissimilarity and interspecies similarity, life-cycle stages, camouflage, diverse imaging conditions, and variability in insect orientation. An end-to-end approach for training deep-learning models, InsectNet, is proposed to address these challenges. Our approach has the following key features: (i) uses a large dataset of insect images collected through citizen science along with label-free self-supervised learning to train a global model, (ii) fine-tuning this global model using smaller, expert-verified regional datasets to create a local insect identification model, (iii) which provides high prediction accuracy even for species with small sample sizes, (iv) is designed to enhance model trustworthiness, and (v) democratizes access through streamlined machine learning operations. This global-to-local model strategy offers a more scalable and economically viable solution for implementing advanced insect identification systems across diverse agricultural ecosystems. We report accurate identification (>96% accuracy) of numerous agriculturally and ecologically relevant insect species, including pollinators, parasitoids, predators, and harmful insects. InsectNet provides fine-grained insect species identification, works effectively in challenging backgrounds, and avoids making predictions when uncertain, increasing its utility and trustworthiness. The model and associated workflows are available through a web-based portal accessible through a computer or mobile device. We envision InsectNet to complement existing approaches, and be part of a growing suite of AI technologies for addressing agricultural challenges.
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Article
Native vegetation embedded in landscapes dominated by corn and soybean improves honey bee health and productivity
2023-03-17
,
Zhang, Ge
,
Murray, Caroline J.
,
St. Clair, Ashley L.
,
Cass, Randall P.
,
Dolezal, Adam G.
,
Schulte Moore, Lisa
,
Toth, Amy
,
O'Neal, Matthew
,
Department of Entomology
,
Natural Resource Ecology and Management
,
Department of Ecology, Evolution, and Organismal Biology (LAS)
,
Pollinator Working Group
1. Balancing demand for food while supporting biodiversity and ecosystem services in landscapes committed to crop production may require integrating conservation with agriculture. Adding strips of diverse, native, perennial vegetation, through the recently created prairie strips practice of the U.S. Conservation Reserve Program, into annual cropland reduces soil and nutrient loss, and supports more diverse and abundant communities of birds and insects, including native pollinators. It remains unclear if prairie strips can reverse declines in the health and productivity of the exotic honey bee in the U.S.
2. This study determined if prairie strips provide floral resources to honey bees and support colony vigor, in a highly farmed landscape with limited perennial habitat. We hypothesized that honey bee health and productivity would be improved if given access to prairie strips, and this hypothesis was tested in a multi-year, replicated, longitudinal study on commercial, conventional farms committed to corn and soybean production with and without prairie strips. We predicted that prairie strips would have more diverse flowering plants, and colonies located in these strips would be healthier and more productive than colonies kept at farms without purposefully established native vegetation (i.e., control fields).
3. We found that prairie strips had more diverse flowering plants and abundant floral resources than control fields. Colonies kept at fields with prairie strips collected 50% more pollen during the growing season (June to September), had a 24% larger end-of-season worker bee populations, and 20% higher overwinter survival than colonies kept at control fields. Furthermore, colonies kept at prairie strips were 24% heavier when they reached their peakweight in August, an indicator of honey production.
4. Honey bees collected pollen from flowering plants found in prairie strips, revealing the potential for interactions with wild pollinators. However, this was limited to 50% of the taxa in prairie strips, suggesting honey bees may not deplete all of the food resources simultaneously used by wild pollinators.
5. Synthesis and applications. Our results suggest that efforts to enhance habitat diversity within croplands with native plants increase honey bee health and productivity while providing multiple additional ecosystem services important to agriculture.
2. This study determined if prairie strips provide floral resources to honey bees and support colony vigor, in a highly farmed landscape with limited perennial habitat. We hypothesized that honey bee health and productivity would be improved if given access to prairie strips, and this hypothesis was tested in a multi-year, replicated, longitudinal study on commercial, conventional farms committed to corn and soybean production with and without prairie strips. We predicted that prairie strips would have more diverse flowering plants, and colonies located in these strips would be healthier and more productive than colonies kept at farms without purposefully established native vegetation (i.e., control fields).
3. We found that prairie strips had more diverse flowering plants and abundant floral resources than control fields. Colonies kept at fields with prairie strips collected 50% more pollen during the growing season (June to September), had a 24% larger end-of-season worker bee populations, and 20% higher overwinter survival than colonies kept at control fields. Furthermore, colonies kept at prairie strips were 24% heavier when they reached their peakweight in August, an indicator of honey production.
4. Honey bees collected pollen from flowering plants found in prairie strips, revealing the potential for interactions with wild pollinators. However, this was limited to 50% of the taxa in prairie strips, suggesting honey bees may not deplete all of the food resources simultaneously used by wild pollinators.
5. Synthesis and applications. Our results suggest that efforts to enhance habitat diversity within croplands with native plants increase honey bee health and productivity while providing multiple additional ecosystem services important to agriculture.
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Article
Insect Floral Visitors of Ptelea trifoliata (Rutaceae) in Iowa, United States
2022
,
Talcott Stewart, A. J.
,
O'Neal, Matthew
,
Graves, William
,
Department of Horticulture
,
Department of Entomology
,
Pollinator Working Group
Ptelea trifoliata L., is a North American tree that supports insect communities through floral rewards. Our objectives were to determine the importance of insects as pollinators of P. trifoliata; describe the community of floral visiting insects of P. trifoliata in Iowa, where no such information was available; and to note insect preferences for male or female flowers. Over two years, inflorescences on 13 trees were covered with mesh bags before blooming and the amount of fruit produced was compared to uncovered inflorescences from the same trees. In one year, insects were collected from male and female trees with an insect vacuum every 3 h between 7 am and 7 pm from four sites in Iowa, USA between 30 May and 16 June 2020. In 2019 and 2020, almost no fruit set occurred from inflorescences covered with mesh bags while an average of 51.2 fruits formed on unbagged inflorescences (P < 0.0001), suggesting insects larger than the 600 μm pore diameters mesh were responsible for pollination of P. trifoliata. Insects from five orders, 49 families, and at least 109 species were collected. Most insects were Hymentoptera (48.3%) or Diptera (28.2%). Male flowers attracted 62.3% of all insects collected. Since most of the insects found visiting P. trifoliata were not bees, the floral rewards of the flowers may be a valuable resource for a wide variety of insects in the central United States.
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Persistent monitoring of insect-pests on sticky traps through hierarchical transfer learning and slicing-aided hyper inference
2024-11-21
,
Fotouhi, Fateme
,
Menke, Kevin
,
Prestholt, Aaron
,
Gupta, Ashish
,
Carroll, Matthew E.
,
Yang, Hsin-Jung
,
Skidmore, Edwin J.
,
O'Neal, Matthew
,
Merchant, Nirav
,
Das, Sajal K.
,
Kyveryga, Peter
,
Ganapathysubramanian, Baskar
,
Singh, Asheesh
,
Singh, Arti
,
Sarkar, Soumik
,
Mechanical Engineering
,
Department of Computer Science
,
Plant Pathology, Entomology and Microbiology
,
Department of Agronomy
Introduction: Effective monitoring of insect-pests is vital for safeguarding agricultural yields and ensuring food security. Recent advances in computer vision and machine learning have opened up significant possibilities of automated persistent monitoring of insect-pests through reliable detection and counting of insects in setups such as yellow sticky traps. However, this task is fraught with complexities, encompassing challenges such as, laborious dataset annotation, recognizing small insect-pests in low-resolution or distant images, and the intricate variations across insect-pests life stages and species classes.
Methods: To tackle these obstacles, this work investigates combining two solutions, Hierarchical Transfer Learning (HTL) and Slicing-Aided Hyper Inference (SAHI), along with applying a detection model. HTL pioneers a multi-step knowledge transfer paradigm, harnessing intermediary in-domain datasets to facilitate model adaptation. Moreover, slicing-aided hyper inference subdivides images into overlapping patches, conducting independent object detection on each patch before merging outcomes for precise, comprehensive results.
Results: The outcomes underscore the substantial improvement achievable in detection results by integrating a diverse and expansive in-domain dataset within the HTL method, complemented by the utilization of SAHI.
Discussion: We also present a hardware and software infrastructure for deploying such models for real-life applications. Our results can assist researchers and practitioners looking for solutions for insect-pest detection and quantification on yellow sticky traps.
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Exploring the Dynamics of Virulent and Avirulent Aphids: A Case for a ‘Within Plant’ Refuge
2022-02
,
O'Neal, Matthew
,
Banerjee, Aniket
,
Valmorbida, Ivair
,
O’Neal, Matthew E.
,
Parshad, Rana
,
Mathematics
,
Department of Entomology
The soybean aphid, Aphis glycines (Hemiptera: Aphididae), is an invasive pest that can cause severe yield loss to soybeans in the North Central United States. A tactic to counter this pest is the use of aphid-resistant soybean varieties. However, the frequency of virulent biotypes that can survive on resistant varieties is expected to increase as more farmers use these varieties. Soybean aphids can alter soybean physiology primarily by two mechanisms, feeding facilitation, and the obviation of resistance, favoring subsequent colonization by additional conspecifics. We developed a nonlocal, differential equation population model to explore the dynamics of these biological mechanisms on soybean plants coinfested with virulent and avirulent aphids. We then use demographic parameters from laboratory experiments to perform numerical simulations via the model. We used this model to determine that initial conditions are an important factor in the season-long cooccurrence of both biotypes. The initial population of both biotypes above the resistance threshold or avirulent aphid close to resistance threshold and high virulent aphid population results in coexistence of the aphids throughout the season. These simulations successfully mimicked aphid dynamics observed in the field- and laboratory-based microcosms. The model showed an increase in colonization of virulent aphids increases the likelihood that aphid resistance is suppressed, subsequently increasing the survival of avirulent aphids. This interaction produced an indirect, positive interaction between the biotypes. These results suggest the potential for a ‘within plant’ refuge that could contribute to the sustainable use of aphid-resistant soybeans.
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Agroecosystem landscape diversity shapes wild bee communities independent of managed honey bee presence
2022
,
St. Clair, Ashley
,
Zhang, Ge
,
Dolezal, Adam
,
O'Neal, Matthew
,
Toth, Amy
,
Department of Entomology
,
Department of Ecology, Evolution, and Organismal Biology (LAS)
,
Pollinator Working Group
Large scale agricultural production can lead to a reduction in availability of habitat used by wild bees for nesting and forage and has been implicated in worldwide bee population declines. There is growing concern that further declines in wild bee populations will occur because of continued transformations of natural or seminatural landscapes into crop monocultures. Managed honey bees, often used for pollination services in agricultural systems, can compete with wild bees and are hypothesized to negatively affect their communities. Although the response of wild bees to both agriculture and honey bees (i.e., apiculture) has been studied, the relative importance of each and their potential interactions on wild bee communities are not well understood. To forecast the extent to which landscape simplification can affect wild bees and to better understand whether honey bee presence in an already disturbed landscape might further exacerbate declines, we conducted a replicated, longitudinal assessment of wild bee community richness and richness of functional guilds (e.g., floral specificity and nesting preference) in an intensively farmed region of the United States where much of the landscape is devoted to monoculture annual crop (maize and soybean) production and managed honey bee colonies co-occur. The presence of a small apiary (4 colonies) had no immediate effect on wild bee richness, suggesting honey beekeeping may not always negatively impact wild bees. Rather, landscape composition analysis showed strong responses of wild bees to land use, with communities being less speciose in landscapes with high proportions of crop production. The availability of woodland and grassland habitat, especially at the local scale (<800 m), was associated with the greatest increase in bee richness especially for rarer aboveground nesting and floral specialist species. These data suggest large scale monocultures have a greater impact on bee communities than the presence of small apiaries. The results of this research provide important information on possible solutions in agroecosystem management to support increased bee diversity where annual crop production and apiculture are practiced. Namely, mitigation of wild bee declines in such agroecosystems may benefit more from the re-integration of landscape biodiversity, with priority on the re-introduction of perennial vegetation, like that found in woodland and grassland habitats, than the restriction of honey bee apiculture.