Sex Pheromone of the Azalea Mealybug: Absolute Configuration and Kairomonal Activity.

The sex pheromone of the azalea mealybug, Crisicoccus azaleae (Tinsley, 1898) (Hemiptera: Pseudococcidae), includes esters of a methyl-branched medium-chain fatty acid, ethyl and isopropyl (E)-7-methyl-4-nonenoate. These compounds are exceptional among mealybug pheromones, which are commonly monoterpenes. Determination of the absolute configuration is challenging, because both chromatographic and spectrometric separations of stereoisomers of fatty acids with a methyl group distant from the carboxyl group are difficult. To solve this problem, we synthesized the enantiomers via the Johnson-Claisen rearrangement to build (E)-4-alkenoic acid by using (R)- and (S)-3-methylpentanal as chiral blocks, which were readily available from the amino acids L-(+)-alloisoleucine and L-(+)-isoleucine, respectively. Each pure enantiomer, as well as the natural pheromone, was subsequently derivatized with a highly potent chiral labeling reagent used in the Ohrui-Akasaka method. Through NMR spectral comparisons of these derivatives, the absolute configuration of the natural pheromone was determined to be S. Field-trap bioassays showed that male mealybugs were attracted more to (S)-enantiomers and preferred the natural stereochemistry. Moreover, the synthetic pheromones attracted Anagyrus wasps, indicating that the azalea mealybug pheromone has kairomonal activity.

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/.

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.

Improving pest monitoring networks using a simulation-based approach to contribute to pesticide reduction.

Conventional pest management mainly relies on the use of pesticides. However, the negative externalities of pesticides are now well known. More sustainable practices, such as Integrated Pest Management, are necessary to limit crop damage from pathogens, pests and weeds in agroecosystems. Reducing pesticide use requires information to determine whether chemical treatments are really needed. Pest monitoring networks (PMNs) are key contributors to this information. However, the effectiveness of a PMN in delivering relevant information about pests depends on its spatial sampling resolution and its memory length. The trade-off between the monitoring efforts and the usefulness of the information provided is highly dependent on pest ecological traits, the damage they can cause (in terms of crop losses), and economic drivers (production costs, agriculture product prices and incentives). Due to the high complexity of optimising PMNs, we have developed a theoretical model that belongs to the family of Dynamic Bayesian Networks in order to compare several PMNs performances. This model links the characteristics of a PMN to treatment decisions and the resulting pest dynamics. Using simulation and inference tools for graphical models, we derived the proportion of impacted fields, the number of pesticide treatments and the overall gross margins for three types of pest with contrasting levels of endocyclism. The term "endocyclic" refers to an organism whose development is mostly restricted to a field and highly depends on the inoculum present in the considered field. The presence of purely endocyclic pests at a given time increases the probability of reoccurrence. Conversely, slightly endocyclic pests have a low persistence. The simulation analysis considered ten scenarios: an expected margin-based strategy with a spatial resolution of four PMNs and two memory lengths (one year or eight years), as well as two extreme crop protection strategies (systematic treatments on all fields and systematic no treatment). For purely and mainly endocyclic pests (e.g. soil-borne pathogens and most weeds, respectively), we found that increasing the spatial resolution of PMNs made it possible to significantly decrease the number of treatments required for pest control. Taking past observations into account was also effective, but to a lesser extent. PMN information had virtually no influence on the control of non-endocyclic pests (such as flying insects or airborne plant pathogens) which may be due to the spatial coverage addressed in our study. The next step is to extend the analysis of PMNs and to integrate the information generated by PMNs into sustainable pest management strategies, both at the field and the landscape level.

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.

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.

Volatiles of Grape Inoculated with Microorganisms: Modulation of Grapevine Moth Oviposition and Field Attraction.

Semiochemicals released by plant-microbe associations are used by herbivorous insects to access and evaluate food resources and oviposition sites. Adult insects may utilize microbial-derived nutrients to prolong their lifespan, promote egg development, and offer a high nutritional substrate to their offspring. Here, we examined the behavioral role of semiochemicals from grape-microbe interactions on oviposition and field attraction of the grapevine moth Lobesia botrana (Denis & Schiffermüller). The volatile constituents released by grape inoculated with yeasts (Hanseniaspora uvarum (Niehaus), Metschnikowia pulcherrima (Pitt.) M.W. Miller, Pichia anomala, Saccharomyces cerevisiae Meyen ex E.C. Hansen, and Zygosaccharomyces rouxii (Boutroux) Yarrow), sour rot bacteria (Acetobacter aceti (Pasteur) Beijerinck and Gluconobacter oxydans (Henneberg) De Ley), and a fungal pathogen (Botrytis cinerea Pers.) all endemic of the vineyard were sampled by solid-phase microextraction and analyzed by gas-chromatography coupled with mass spectrometry. Ethanol, 3-methyl-1-butanol, and ethyl acetate were the most common volatiles released from all microbe-inoculated grapes. In addition, acetic acid was released at a substantial amount following bacteria inoculation and in a three-way inoculation with yeasts and the fungus. 2-phenylethanol, a compound reported to attract tortricid moths when used in combination with acetic acid, was found at a relatively low level in all microbial combinations as well as in the control grape. While grapes inoculated with a consortium of yeasts stimulated oviposition in comparison with uninoculated berries, the phytopathogenic fungus deterred egg-laying. Nonetheless, the highest preference to lay eggs was measured when the yeasts were co-inoculated with the fungus. The lowest preference was obtained when grapes were inoculated with sour rot bacteria and their binary co-inoculation with yeasts and the fungus. Interestingly, oviposition on berries simultaneously inoculated with all the three microbial groups was unaffected. Lures loaded with either acetic acid or 2-phenylethanol were not attractive when placed in traps as single component in vineyards, but a binary blend attracted both sexes of grapevine moth in significant numbers. Further addition of the three most common volatiles released by infected berries (ethanol, 3-methyl-1-butanol, and ethyl acetate) did not significantly increase moth catch with this binary blend. The ecological implications of the grape-microorganism and grapevine moth interaction as well as the possibility to develop a pest monitoring system based on microbial volatiles are discussed.

Towards understanding temporal and spatial dynamics of Bactrocera oleae (Rossi) infestations using decade-long agrometeorological time series.

Insect dynamics depend on temperature patterns, and therefore, global warming may lead to increasing frequencies and intensities of insect outbreaks. The aim of this work was to analyze the dynamics of the olive fruit fly, Bactrocera oleae (Rossi), in Tuscany (Italy). We profited from long-term records of insect infestation and weather data available from the regional database and agrometeorological network. We tested whether the analysis of 13 years of monitoring campaigns can be used as basis for prediction models of B. oleae infestation. We related the percentage of infestation observed in the first part of the host-pest interaction and throughout the whole year to agrometeorological indices formulated for different time periods. A two-step approach was adopted to inspect the effect of weather on infestation: generalized linear model with a binomial error distribution and principal component regression to reduce the number of the agrometeorological factors and remove their collinearity. We found a consistent relationship between the degree of infestation and the temperature-based indices calculated for the previous period. The relationship was stronger with the minimum temperature of winter season. Higher infestation was observed in years following warmer winters. The temperature of the previous winter and spring explained 66 % of variance of early-season infestation. The temperature of previous winter and spring, and current summer, explained 72 % of variance of total annual infestation. These results highlight the importance of multiannual monitoring activity to fully understand the dynamics of B. oleae populations at a regional scale.

Reducing pest pressure and insecticide use by increasing hedgerows in the landscape.

Reducing pesticide use while maintaining agricultural production is a key challenge. Ecological theory predicts that landscape simplification is likely to increase insect pest outbreaks and limit their control by natural enemies, and this situation could boost insecticide use. Some studies have indeed detected that simpler landscapes were associated with higher insecticide use, but very few have demonstrated that this association is caused by landscape effects on pest abundance. Here, we analysed insecticide use and pest pressure in response to landscape simplification across 557 arable farms across France. Accounting for potentially confounding covariates, we found that lower cover of hedgerows in the landscape, but not semi natural areas, were associated with higher on-farm insecticide use. We also found that greater hedgerow coverage was associated with lower aphid pest pressure. Specifically, increasing the landscape-scale cover of hedgerows from 1 % to 3 % meant that insecticide use was halved. These findings suggest that restoring hedgerow cover at the landscape scale should be targeted in order to speed-up the ecological intensification of agriculture.

An annotated image dataset of pests on different coloured sticky traps acquired with different imaging devices.

The sticky trap is probably the most cost-effective tool for catching insect pests, but the identification and counting of insects on sticky traps is very labour-intensive. When investigating the automatic identification and counting of pests on sticky traps using computer vision and machine learning, two aspects can strongly influence the performance of the model - the colour of the sticky trap and the device used to capture the images of the pests on the sticky trap. As far as we know, there are no available image datasets to study these two aspects in computer vision and deep learning algorithms. Therefore, this paper presents a new dataset consisting of images of two pests commonly found in post-harvest crops - the red flour beetle (Tribolium castaneum) and the rice weevil (Sitophilus oryzae) - captured with three different devices (DSLR, webcam and smartphone) on blue, yellow, white and transparent sticky traps. The images were sorted by device, colour and species and divided into training, validation and test parts for the development of the deep learning model.

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.

First use of unmanned aerial vehicles to monitor Halyomorpha halys and recognize it using artificial intelligence.

BACKGROUND: Halyomorpha halys is one of the most damaging invasive agricultural pests in North America and southern Europe. It is commonly monitored using pheromone traps, which are not very effective because few bugs are caught and some escape and/or remain outside the trap on surrounding plants where they feed, increasing the damage. Other monitoring techniques are based on visual sampling, sweep-netting and tree-beating. However, all these methods require several hours of human labor and are difficult to apply to large areas. The aim of this work is to develop an automated monitoring system that integrates image acquisition through the use of drones with H. halys detection through the use of artificial intelligence (AI). RESULTS: The study results allowed the development of an automated flight protocol using a mobile app to capture high-resolution images. The drone caused only low levels of disturbance in both adult and intermediate instars, inducing freezing behavior in adults. Each of the AI models used achieved very good performance, with a detection accuracy of up to 97% and recall of up to 87% for the X-TL model. CONCLUSION: The first application of this novel monitoring system demonstrated the potential of drones and AI to detect and quantify the presence of H. halys. The ability to capture high-altitude, high-resolution images makes this method potentially suitable for use with a range of crops and pests. © 2024 Society of Chemical Industry.

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.

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.

Optimizing a rapid LAMP assay for discrimination of Drosophila suzukii (Diptera: Drosophilidae) from common drosophilids captured in monitoring traps from the Midwest, United States.

Spotted-wing Drosophila, Drosophila suzukii, is an economically important pest of small fruits worldwide. Currently, the timing of management strategies relies on detection of adult flies captured in baited monitoring traps; however, identifying D. suzukii in trap catch based on morphology can be challenging for growers. DNA-based diagnostic methods such as loop-mediated isothermal amplification (LAMP) have the potential to improve D. suzukii detection. This study evaluated a LAMP assay as a diagnostic tool to discriminate between D. suzukii and closely related drosophilid species found commonly in monitoring traps in the Midwestern United States. Targeting the mitochondrial cytochrome oxidase I (COI) gene, we found the LAMP assay accurately detected D. suzukii with as little as 0.1 ng/µl of DNA at 63 °C for 50 min. Under these optimal incubation conditions, D. suzukii could be discriminated from D. affinis and D. simulans consistently, when specimens collected from liquid monitoring traps were tested independently. Compared to other DNA-based diagnostic tools for D. suzukii, LAMP offers unique benefits: DNA extraction is not required, testing occurs at one temperature in less than 1 h, and positive results are visible as a colorimetric change from pink to yellow. The LAMP assay for D. suzukii can reduce reliance on morphological identification, enhance the adoption of monitoring tools, and improve accuracy of detection. Further optimization can be conducted to evaluate the accuracy and sensitivity of results when a mixture of DNA from both D. suzukii and congener flies are tested in a single LAMP reaction.

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.

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.

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.

Nationwide Termite Pest Survey Conducted in Taiwan as a Citizen Science Project.

Information regarding the species composition and dispersal flight season of termites is crucial for termite management. The major obstacles to collecting such information are a lack of access to private buildings and shortage of workers to monitor and report on termite swarming. To overcome these difficulties, we launched a citizen science project in which members of the public and pest management professionals were invited to collect termite samples. We created the website, Taiwan Termite Identification Service, on which populace could log the collection information, and ship termite samples to our laboratory for identification. We also established a Facebook group, called the "Termite Forum," to publicize this project. A total of 3024 samples were collected from 2015 to 2020, and we identified the species of >93% of the samples. Based on 1499 samples collected from buildings, five structural termite pests were identified, and species composition in each county of Taiwan is available. According to 844 dispersal flight events, termite dispersal flight timing peak and degree of centralization were estimated using a Gaussian model. The collected data demonstrated that the invasive termite species, Coptotermes gestroi (Wasmann) (Blattodea: Rhinotermitidae), continued northward expansion. The first intercepted alate of Schedorhinotermes sp. (Blattodea: Rhinotermitidae) indicated that it may be a new invasive pest from Southeast Asia. This study reports on a successful case of a citizen science project where urban pest data were collected on a national scale.

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.