Does monitoring pests pay off? a bioeconomic assessment of Drosophila suzukii controls

BACKGROUND

Drosophila suzukii is a significant invasive pest that has caused high management costs and economic losses for blueberry growers in the United States. The status quo control strategy commonly used by growers is to apply pesticides proactively and frequently to reduce infestation. Recent studies have shown that the calendar-based spraying strategy might be unsustainable in the long term, making the reduction of pesticide reliance a top priority for the berry industry. Incorporating pest monitoring into the control strategy could be an option to improve efficiency while reducing pesticide usage. This study assesses the economic implications of monitoring-based control strategies compared to calendar-based spraying control strategies for organic blueberry production in Oregon. We combine a D. suzukii population model into the economic simulation framework, evaluate two monitoring methods (adult trapping and fruit sampling), and identify the profit-maximizing control strategy under different scenarios.

RESULTS

In the baseline scenario, control strategies that incorporate fruit sampling exhibit the highest average profits. Although the status quo control strategy (spraying every 3 days) generates higher average revenue than monitoring-based strategies, the cost from the higher number of pesticide application offsets the returns.

CONCLUSION

This study uses a novel bioeconomic simulation framework to show that incorporating fruit sampling can be a promising tool to reduce pesticide reliance while controlling D. suzukii infestation. These findings provide clearer information on the economic viability of using monitoring-based pest control strategies in organic berry production, and the assessment framework sheds light on the economics of pest management. © 2023 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

A Decade of Hidden Phytoplasmas Unveiled Through Citizen Science

Climate change is impacting agriculture in many ways, and a contribution from all is required to reduce the imminent losses related to it. Recently, it has been shown that citizen science could be a way to trace the impact of climate change. However, how can citizen science be applied in plant pathology? Here, using as an example a decade of phytoplasma-related diseases reported by growers, agronomists, and citizens in general, and confirmed by a government laboratory, we explored how to better value plant pathogen monitoring data. Through this collaboration, we found that in the last decade, 34 hosts have been affected by phytoplasmas; 9, 13, and 5 of these plants were, for the first time, reported phytoplasma hosts in eastern Canada, all of Canada, and worldwide, respectively. Another finding of great impact is the first report of a 'Candidatus Phytoplasma phoenicium'-related strain in Canada, while 'Ca. P. pruni' and 'Ca. P. pyri' were reported for the first time in eastern Canada. These findings will have a great impact on the management of phytoplasmas and their insect vectors. Using these insect-vectored bacterial pathogens, we show the need for new strategies that can allow fast and accurate communication between concerned citizens and those institutions confirming their observations.[Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

The Database of European Forest Insect and Disease Disturbances: DEFID2

Insect and disease outbreaks in forests are biotic disturbances that can profoundly alter ecosystem dynamics. In many parts of the world, these disturbance regimes are intensifying as the climate changes and shifts the distribution of species and biomes. As a result, key forest ecosystem services, such as carbon sequestration, regulation of water flows, wood production, protection of soils, and the conservation of biodiversity, could be increasingly compromised. Despite the relevance of these detrimental effects, there are currently no spatially detailed databases that record insect and disease disturbances on forests at the pan-European scale. Here, we present the new Database of European Forest Insect and Disease Disturbances (DEFID2). It comprises over 650,000 harmonized georeferenced records, mapped as polygons or points, of insects and disease disturbances that occurred between 1963 and 2021 in European forests. The records currently span eight different countries and were acquired through diverse methods (e.g., ground surveys, remote sensing techniques). The records in DEFID2 are described by a set of qualitative attributes, including severity and patterns of damage symptoms, agents, host tree species, climate-driven trigger factors, silvicultural practices, and eventual sanitary interventions. They are further complemented with a satellite-based quantitative characterization of the affected forest areas based on Landsat Normalized Burn Ratio time series, and damage metrics derived from them using the LandTrendr spectral-temporal segmentation algorithm (including onset, duration, magnitude, and rate of the disturbance), and possible interactions with windthrow and wildfire events. The DEFID2 database is a novel resource for many large-scale applications dealing with biotic disturbances. It offers a unique contribution to design networks of experiments, improve our understanding of ecological processes underlying biotic forest disturbances, monitor their dynamics, and enhance their representation in land-climate models. Further data sharing is encouraged to extend and improve the DEFID2 database continuously. The database is freely available at https://jeodpp.jrc.ec.europa.eu/ftp/jrc-opendata/FOREST/DISTURBANCES/DEFID2/.

Evaluating spotted lanternfly (Hemiptera: Fulgoridae) infestation in the Northern Ohio Valley

Lycorma delicatula White (spotted lanternfly; SLF) is an invasive pest insect threatening increased agricultural costs as it spreads rapidly westward across the United States. As such, surveying was conducted adjacent to the insect's westernmost quarantine area in 2021-2022 to support multi-state monitoring. Specifically, 2,077 visual and sticky-trap surveys were performed in 13 repeatedly surveyed plots strategically located near high-traffic roadways and rail-lines along the Ohio-West Virginia border. Sites were located in Jefferson (Ohio), Brooke (West Virginia), and Hancock (West Virginia) counties. Only one SLF was detected in 2021 (the third documented Ohio site containing SLF) in close proximity to a railway, consistent with rail-mediated dispersal trends recorded throughout the United States. Thirty-one SLF were captured in 2 Ohio sites in 2022, 30 of which were captured at the same railway site as in 2021. However, 1 of the 31 SLF was found in a plot on a university campus 1.25 km from the nearest railway, along with 10 additional specimens found in a follow-up visual survey of a neighboring woodlot. Failure to detect SLF at nearby survey plots nearer to the closest rail line and commuter parking lots suggests local unaided dispersal in a state with primarily train-mediated dispersal-mirroring trends in affected states with more established SLF populations. Data from this survey are valuable for establishing baselines and early-invasion patterns of SLF dispersal into Ohio, anticipating SLF expansion patterns in Ohio, and eventually contributing to improved SLF dispersal modeling in Ohio, the Midwest, and the United States.

Flight Phenology of Elasmopalpus lignosellus (Lepidoptera: Pyralidae) in the Northwest Florida Panhandle

Elasmopalpus lignosellus Zeller (Lepidoptera: Pyralidae), the lesser cornstalk borer (LCSB), is an economically important peanut pest in the southeastern U.S. region, and its occurrence and abundance have been associated with warm and dry conditions. In the Northwestern Florida Panhandle (USA), the LCSB occurrence and abundance are unknown. Thus, a study in this region used commercial sex pheromones to capture male moths year-round from July/2017 to June/2021. Our results indicated that the LCSBs were present in the region from April to December, with higher abundance in August. Moths were also caught from January to March in only 2020. In addition, the number of moths collected increased when the temperature increased. Our results indicate a different pattern for LCSB abundance than previously documented, with peak occurrence in warm and wet conditions (August). These results support that region-specific weather information should be considered when designing IPM recommendations based on the phenology of pest occurrence in the agroecosystem.

Big Data and Machine Learning to Improve European Grapevine Moth (Lobesia botrana) Predictions

Machine Learning (ML) techniques can be used to convert Big Data into valuable information for agri-environmental applications, such as predictive pest modeling. Lobesia botrana (Denis & Schiffermüller) 1775 (Lepidoptera: Tortricidae) is one of the main pests of grapevine, causing high productivity losses in some vineyards worldwide. This work focuses on the optimization of the Touzeau model, a classical correlation model between temperature and L. botrana development using data-driven models. Data collected from field observations were combined with 30 GB of registered weather data updated every 30 min to train the ML models and make predictions on this pest's flights, as well as to assess the accuracy of both Touzeau and ML models. The results obtained highlight a much higher F1 score of the ML models in comparison with the Touzeau model. The best-performing model was an artificial neural network of four layers, which considered several variables together and not only the temperature, taking advantage of the ability of ML models to find relationships in nonlinear systems. Despite the room for improvement of artificial intelligence-based models, the process and results presented herein highlight the benefits of ML applied to agricultural pest management strategies.

Optimization of Sensors Data Transmission Paths for Pest Monitoring Based on Intelligent Algorithms

The harm of agricultural pests presents a remarkable effect on the quality and safety of edible farm products and the monitoring and identification of agricultural pests based on the Internet of Things (IoT) produce a large amount of data to be transmitted. To achieve efficient and real-time transmission of the sensors' data for pest monitoring, this paper selects 235 geographic coordinates of agricultural pest monitoring points and uses genetic algorithm (GA), particle swarm optimization (PSO), and simulated annealing (SA) to optimize the data transmission paths of sensors. The three intelligent algorithms are simulated by MATLAB software. The results show that the optimized path based on PSO can make the shortest time used for transmitting data, and its corresponding minimum time is 4.868012 s. This study can provide a reference for improving the transmission efficiency of agricultural pest monitoring data, provide a guarantee for developing real-time and effective pest control strategies, and further reduce the threat of pest damage to the safety of farm products.

Community of Bark and Ambrosia Beetles (Coleoptera: Curculionidae: Scolytinae and Platypodinae) in Agricultural and Forest Ecosystems with Laurel Wilt

Redbay ambrosia beetle, Xyleborus glabratus, is an invasive wood-boring pest first detected in the USA in 2002 in Georgia. The beetle's dominant fungal symbiont, Harringtonialauricola, causes laurel wilt, a lethal disease of trees in the Lauraceae. Over the past 20 years, X. glabratus and laurel wilt have spread to twelve southeastern states, resulting in high mortality of native Persea species, including redbay (P. borbonia), swampbay (P. palustris), and silkbay (P. humilis). Laurel wilt also threatens avocado (P. americana) in south Florida, but in contrast to the situation in forests, X. glabratus is detected at very low levels in affected groves. Moreover, other species of ambrosia beetle have acquired H. lauricola and now function as secondary vectors. To better understand the beetle communities in different ecosystems exhibiting laurel wilt, parallel field tests were conducted in an avocado grove in Miami-Dade County and a swampbay forest in Highlands County, FL. Sampling utilized ethanol lures (the best general attractant for ambrosia beetles) and essential oil lures (the best attractants for X. glabratus), alone and in combination, resulting in detection of 20 species. This study documents host-related differences in beetle diversity and population levels, and species-specific differences in chemical ecology, as reflected in efficacy of lures and lure combinations.

Automatic Crop Pest Detection Oriented Multiscale Feature Fusion Approach

Specialized pest control for agriculture is a high-priority agricultural issue. There are multiple categories of tiny pests, which pose significant challenges to monitoring. Previous work mainly relied on manual monitoring of pests, which was labor-intensive and time-consuming. Recently, deep-learning-based pest detection methods have achieved remarkable improvements and can be used for automatic pest monitoring. However, there are two main obstacles in the task of pest detection. (1) Small pests often go undetected because much information is lost during the network training process. (2) The highly similar physical appearances of some categories of pests make it difficult to distinguish the specific categories for networks. To alleviate the above problems, we proposed the multi-category pest detection network (MCPD-net), which includes a multiscale feature pyramid network (MFPN) and a novel adaptive feature region proposal network (AFRPN). MFPN can fuse the pest information in multiscale features, which significantly improves detection accuracy. AFRPN solves the problem of anchor and feature misalignment during RPN iterating, especially for small pest objects. In extensive experiments on the multi-category pests dataset 2021 (MPD2021), the proposed method achieved 67.3% mean average precision (mAP) and 89.3% average recall (AR), outperforming other deep learning-based models.

Improving invasive species management using predictive phenology models: an example from brown marmorated stink bug (Halyomorpha halys) in Japan

BACKGROUND

In order to better understand the population dynamics of invasive species in their native range, we developed two predictive phenological models using the ubiquitous invasive insect pest, Halyomorpha halys (Stål) (Hemiptera: Pentatomidae), as the model organism. Our work establishes a zero-inflated negative binomial regression (ZINB) model, and a general additive mixed model (GAMM) based on 11 years of black light trap monitoring of H. halys at three locations in Japan.

RESULTS

The ZINB model indicated that degree days (DD) have a significant effect on the trap catch of adult H. halys, and that precipitation has no effect. A dataset generated by 1000 simulations from the ZINB suggested that higher predicted trap catches equated to a lower probability of encountering a zero-count. The GAMM produced a cubic regression smooth curve which forecasts the seasonal phenology of H. halys as following a bell-shaped trend in Japan. Critical DD points during the field season in Japan included 261 DD for first H. halys adult detection and 1091 DD for peak activity.

CONCLUSIONS

This study establishes the first models capable of forecasting native H. halys population dynamics based on DD. These robust models practically improve population forecasting of H. halys in the future and help fill gaps in knowledge pertaining to its native phenology, thus ultimately contributing to the progression of efficient management of this globally invasive species. © 2021 Society of Chemical Industry.

Field specific monitoring of cereal yellow dwarf virus aphid vectors and factors influencing their immigration within fields

BACKGROUND

Neonicotinoid insecticide seed treatments were withdrawn from use on cereal crops in the European Union (EU) in 2018 exposing the crops to yellow dwarf viruses transmitted by cereal aphids. To reduce prophylactic pyrethroid sprays there is a need for easier, field-specific monitoring techniques given that pest incidence is spatially and temporally highly sporadic. A field-specific monitoring method based on the use of yellow sticky traps mounted horizontally just above the crop was developed and evaluated to determine: (i) predictive capabilities of the sticky trap system, (ii) practicalities of use by farmers and agronomists, and (iii) whether landscape composition, boundary type and type of tillage affect immigration of aphid vectors.

RESULTS

Yellow sticky traps effectively sampled winged cereal aphids and identified spatial differences in immigration patterns within- and between fields. Farmers and agronomist's aphid identification skills need improving, although they could detect aphid trends with minimal training. At least three times more cereal aphids were captured in crop headlands, especially next to taller field boundaries indicating that wind currents determined aphid immigration patterns within fields. Considerable between field aphid immigration was detected (24% of fields had no aphid immigration) even on the same farm. Levels of immigrating grain aphids were positively related to the proportion of grassland in the landscape. Tillage type had no impact on levels of immigrating aphids.

CONCLUSION

Field-based monitoring and different management of headland areas could be used to reduce insecticide usage when controlling of cereal/barley yellow dwarf virus.

Automatic Pest Counting from Pheromone Trap Images Using Deep Learning Object Detectors for Matsucoccus thunbergianae Monitoring

Simple Summary

The black pine bast scale, Matsucoccus thunbergianae, is a forest pest that causes widespread damage to black pine; therefore, monitoring this pest is necessary to minimize environmental and economic losses in forests. However, monitoring insects in pheromone traps performed by humans is labor intensive and time consuming. To develop an automated monitoring system, we aimed to develop algorithms that detect and count M. thunbergianae from images of pheromone traps using deep-learning-based object detection algorithms. Object detection models based on deep learning neural networks under various conditions were trained, and the performances of detection and counting were compared and evaluated. In addition, the models were trained to detect small objects well by cropping images into multiple windows. As a result, the algorithms based on deep learning neural networks successfully detected and counted M. thunbergianae. These results showed that accurate and constant pest monitoring is possible using the artificial-intelligence-based methods we proposed.

Abstract

The black pine bast scale, M. thunbergianae, is a major insect pest of black pine and causes serious environmental and economic losses in forests. Therefore, it is essential to monitor the occurrence and population of M. thunbergianae, and a monitoring method using a pheromone trap is commonly employed. Because the counting of insects performed by humans in these pheromone traps is labor intensive and time consuming, this study proposes automated deep learning counting algorithms using pheromone trap images. The pheromone traps collected in the field were photographed in the laboratory, and the images were used for training, validation, and testing of the detection models. In addition, the image cropping method was applied for the successful detection of small objects in the image, considering the small size of M. thunbergianae in trap images. The detection and counting performance were evaluated and compared for a total of 16 models under eight model conditions and two cropping conditions, and a counting accuracy of 95% or more was shown in most models. This result shows that the artificial intelligence-based pest counting method proposed in this study is suitable for constant and accurate monitoring of insect pests.

Field Responses of Rice Stalk Stink Bug, Tibraca limbativentris, to Synthetic Sex Pheromone and Isomers of 1,10-Bisaboladien-3-ol

The rice stalk stink bug, Tibraca limbativentris Stål, is an important rice pest in Brazil, causing significant damage to rice plants and consequently yield losses, with a high invasive potential in Mexico and USA. The male-produced sex pheromone of this species was recently identified as a 7:3 mixture of (3S,6S,7R)-1,10-bisaboladien-3-ol (1) and (3R,6S,7R)-1,10-bisaboladien-3-ol (5) (a.k.a. zingiberenols). The aim of this study was to evaluate field responses of T. limbativentris females to the racemic mixture and stereoisomers of 1,10-bisaboladien-3-ol, including the male-produced sex pheromone. The results obtained in two rice-producing areas of Brazil (Rio Grande do Sul and Santa Catarina) showed that traps baited with the main component 1 alone, the racemic mixture, and a mixture of 1 and 5 were attractive to females of T. limbativentris. The minor component 5 was unable to attract females when used alone. The results indicate that the sex pheromone of T. limbativentris and racemic mixture of 1,10-bisaboladien-3-ol were equally attractive to co-specific females in rice fields, and they could be a tool to incorporate in rice stalk stink bug management programs.

A Laboratory Diet-Overlay Bioassay to Monitor Resistance in Lygus lineolaris (Hemiptera: Miridae) to Insecticides Commonly Used in the Mississippi Delta

A laboratory, diet-overlay pesticide bioassay was developed using a susceptible population of the tarnished plant bug, Lygus lineolaris (Palisot de Beauvois), to study its susceptibility to neonicotinoid, sulfoxamine, organophosphate, and pyrethroid insecticides (thiamethoxam, sulfoxaflor, acephate, and permethrin, respectively). The diet-overlay bioassay was compared to the traditional glass-vial surface residue bioassay. We measured LC50 values by feeding tarnished plant bug adults known doses of insecticides dispensed on top of diet in a 10% solution of honey water for thiamethoxam and 10% acetone in water solutions for permethrin, acephate, and sulfoxaflor. Both the diet-overlay and glass-vial bioassays used dose-response (mortality) regression lines to calculate LC50 values for each insecticide at 6-, 24-, 48-, and 72-h post-exposure. Data variability from the glass-vial bioassay was higher for permethrin, sulfoxaflor, and thiamethoxam than the diet-overlay bioassay, for all evaluation times. In contrast, there was lower variability among replicates to acephate in the glass-vial assay compared to the diet-overlay assay. Control mortalities observed on diet-overlay bioassay were lower (0-5%) than those observed on the glass-vial bioassay (4-27%). The use of green beans, floral-foam, rolling glass vials, and insect handling made the existing standard method tedious to manipulate and difficult to handle large numbers of individuals. The nonautoclaved solid diet provides an opportunity to significantly reduce cost and variability associated with procedures of other bioassay methods. In general, the baseline data provide a basis for future comparison to determine changes in resistance over time.

Assessment of Available Tools for Monitoring Wheat Midge (Diptera: Cecidomyiidae)

Wheat midge, Sitodiplosis mosellana Géhin, is an invasive pest of wheat, Triticum aestivum L. (Poaceae) throughout Canada and the United States. The applicability of available monitoring tools, including sex-pheromone baited traps, yellow sticky cards, and soil core sample surveys, in the northern-most agroecosystem of its invasive range has not been assessed. In this study, the attraction of male wheat midge to two Delta traps (green and orange) baited with one of three pheromone lures (a flex lure and two red septa lures from different sources) were compared. The efficacy of three yellow sticky cards (7 × 12 cm, 14 × 18 cm, and 14 × 18 cm rolled into a cylinder) for capture of male and female midge was assessed. Larvae were extracted from wheat heads sampled at the same sites to determine relationships with earlier adult trap capture. More male adult midges were captured in pheromone-baited traps with a greater surface area and in traps baited with the Scotts flex lure than the Great Lakes IPM septa lure, which had higher and more variable pheromone release rates. The smaller yellow sticky cards captured more male and female midges than the larger yellow sticky cards, regardless of shape. The number of female midges captured on yellow sticky cards predicted the number of larvae in wheat heads. The number of male midges captured in pheromone-baited traps did not predict larval density. Relationships were found between the number of overwintering cocoons recovered in soil core samples and emerging midges the following spring.

Development of an Optoelectronic Sensor for Detecting and Classifying Fruit Fly (Diptera: Tephritidae) for Use in Real-Time Intelligent Traps

Fruit flies (Diptera: Tephritidae) cause losses to world fruit growing. For a fast and effective control of the pest, it is necessary to identify the species and their populations. Thus, we developed an infrared optoelectronic sensor using phototransistors to capture the signal of the partial occlusion of the infrared light caused by the beating of the fly wings. Laboratory experiments were conducted using the sensor to capture the wing beat signal of A. fraterculus and C. capitata. The captured signals were used to obtain the characteristics of the flies' wing beats frequency and for a production of a dataset made available as one of the results of this work. For the passage detection, we developed the algorithm of detection of events of passage (PEDA) that uses the root mean square (RMS) value of a sliding window applied to the signal compared to a threshold value. We developed the algorithm of detection of events of passage (CAEC) that uses the techniques of autocorrelation and Fourier transform for the extraction of the characteristics of the wings' beat signal. The results demonstrate that it is possible to use the sensor for the development of an intelligent trap with detection and classification in real time for A. fraterculus and C. capitata using the wing beat frequency obtained by the developed sensor.

Population Distribution and Range Expansion of the Invasive Mexican Rice Borer (Lepidoptera: Crambidae) in Louisiana

The Mexican rice borer, Eoreuma loftini (Dyar) (Lepidoptera: Crambidae), is an invasive pest that was first introduced into southern Texas in 1980 and has been expanding its range eastward along the United States Gulf Coast. The pest attacks rice (Oryza sativa L.), sugarcane (Saccharum spp.), corn (Zea mays L.), and other graminaceous crops, and its establishment in Louisiana is expected to have severe economic impacts on crop production. Range expansion and population distribution of E. loftini were monitored with a network of 77 pheromone traps throughout southwestern Louisiana from 2013 to 2015. Eoreuma loftini was ubiquitous throughout the study region, with male moths captured in every habitat sampled. Spatial analysis revealed the population is characterized by high and low density clusters, with the greatest trap captures occurring in southeastern Calcasieu Parish and southern Jefferson Davis Parish. Trap captures in more northern regions of the study were lower than in southern parishes. Trap captures in areas where the pest has been established for >3 yr were greatest in rice habitats. The weighted mean population center moved eastward at a rate of ∼11 km per year. Human-aided movement of E. loftini was probably not involved in the eastward expansion documented during this study. Seasonal population peaks were detected in March-April, July-August, and October-November. This study indicates this species is continuing its spread eastward along the United States Gulf Coast and will likely become established throughout Louisiana within the next 20 yr.

Estimating E-Race European Corn Borer (Lepidoptera: Crambidae) Adult Activity in Snap Bean Fields Based on Corn Planting Intensity and Their Activity in Corn in New York Agroecosystems

European corn borer, Ostrinia nubilalis (Hübner), is a major pest of processing snap bean because larvae are contaminants in pods. The incidence of O. nubilalis-contaminated beans has become uncommon in New York, possibly because widespread adoption of Bt field corn has suppressed populations. Snap bean fields located where Bt corn has been intensively grown in space and time may be at lower risk for O. nubilalis than fields located where Bt corn is not common. To manage O. nubilalis infestation risk, growers determine insecticide application frequency in snap bean based on pheromone-trapping information in nearby sweet corn fields; adult activity is presumed equivalent in both crops. Our goal was to determine if corn planting intensity and adult activity in sweet corn could be used to estimate O. nubilalis populations in snap bean in New York in 2014-2015. Numbers of O nubilalis adults captured in pheromone-baited traps located in snap bean fields where corn was and was not intensively grown were similar, suggesting that O. nubilalis does not respond to local levels of Bt corn in the landscape. Numbers of Ostrinia nubilalis captured in pheromone-baited traps placed by snap bean fields and proximal sweet corn fields were not related, indicating that snap bean growers should no longer make control decisions based on adult activity in sweet corn. Our results also suggest that the risk of O. nubilalis infestations in snap bean is low (∼80% of the traps caught zero moths) and insecticide applications targeting this pest should be reduced or eliminated.