Dynamics of pollen beetle (Brassicogethes aeneus) immigration and colonization of oilseed rape (Brassica napus) in Europe.

BACKGROUND: Understanding the dynamics of pest immigration into an agroecosystem enables effective and timely management strategies. The pollen beetle (Brassicogethes aeneus) is a primary pest of the inflorescence stages of oilseed rape (Brassica napus). This study investigated the spatial and temporal dynamics of pollen beetle immigration into oilseed rape fields in Denmark and the UK using multiple methods, including optical sensors. RESULTS: In all fields, pollen beetles were found to be aggregated and beetle density was related to plant growth stage, with more beetles occurring on plants after the budding stage than before inflorescence development. Optical sensors were the most efficient monitoring method, recording pollen beetles 2 and 4 days ahead of water traps and counts from plant scouting, respectively. CONCLUSION: Optical sensors are a promising tool for early warning of insect pest immigration. The aggregation pattern of pollen beetles post immigration could be used to precisely target control in oilseed rape crops. © 2023 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Automating insect monitoring using unsupervised near-infrared sensors.

Insect monitoring is critical to improve our understanding and ability to preserve and restore biodiversity, sustainably produce crops, and reduce vectors of human and livestock disease. Conventional monitoring methods of trapping and identification are time consuming and thus expensive. Automation would significantly improve the state of the art. Here, we present a network of distributed wireless sensors that moves the field towards automation by recording backscattered near-infrared modulation signatures from insects. The instrument is a compact sensor based on dual-wavelength infrared light emitting diodes and is capable of unsupervised, autonomous long-term insect monitoring over weather and seasons. The sensor records the backscattered light at kHz pace from each insect transiting the measurement volume. Insect observations are automatically extracted and transmitted with environmental metadata over cellular connection to a cloud-based database. The recorded features include wing beat harmonics, melanisation and flight direction. To validate the sensor's capabilities, we tested the correlation between daily insect counts from an oil seed rape field measured with six yellow water traps and six sensors during a 4-week period. A comparison of the methods found a Spearman's rank correlation coefficient of 0.61 and a p-value = 0.0065, with the sensors recording approximately 19 times more insect observations and demonstrating a larger temporal dynamic than conventional yellow water trap monitoring.