Challenges in Developing a Real-time Bee-counting Radar

MDPI Sensors journal paper published on 01/06/2023: "Detailed within is an attempt to implement a real-time radar signal classification system to monitor and count bee activity at the hive entry. There is interest in keeping records of the productivity of honeybees. Activity at the entrance can be a good measure of overall health and capacity, and a radar-based approach could be cheap, low power, and versatile, beyond other techniques. Fully automated systems would enable simultaneous, large-scale capturing of bee activity patterns from multiple hives, providing vital data for ecological research and business practice improvement. Data from a Doppler radar were gathered from managed beehives on a farm. Recordings were split into 0.4 s windows, and Log Area Ratios (LARs) were computed from the data. Support vector machine models were trained to recognize flight behavior from the LARs, using visual confirmation recorded by a camera. Spectrogram deep learning was also investigated using the same data. Once complete, this process would allow for removing the camera and accurately counting the events by radar-based machine learning alone. Challenging signals from more complex bee flights hindered progress. System accuracy of 70% was achieved, but clutter impacted the overall results requiring intelligent filtering to remove environmental effects from the data."Open Access. Funded by the Knowledge Econ- omy Skills Scholarships (KESS 2, Ref: BUK2E001) Welsh European Funding Office (WEFO): c81133

Examining Risks to Honey Bee Pollinators Foraging in Agricultural Landscapes

Bee pollinators provide essential ecological services to wild plant communities, and addtremendous economic value to agriculture by improving both the quality and quantity of crop yield. Beekeepers are often contracted by growers to provide colonies of honey bees for pollination of high-value produce (fruits, vegetables and nuts). Many of the major commodity crops produced in the central and mid-southern United States are wind-pollinated (rice, corn, grain sorghum, wheat), or are sufficiently self-fertile (soybeans, cotton), and so do not require bee pollination in order to produce yield. Beekeepers still rely on these agricultural landscapes to support honey bee colonies when not actively pollinating farms or orchards because these landscapes remain irrigated and productive while other areas may endure a long seasonal nectar dearth. However, intensely managed agricultural landscapes can also expose bees to a variety of detrimental risks, including reduced plant diversity and nutrition, and increased pesticide exposure. Neonicotinoid insecticides have been blamed for recent widespread losses of honey bee colonies in the U.S. and abroad. The planting of insecticide-coated seeds to protect plant growth from early season insect damage has come under particular scrutiny as a potentially significant factor in honey bee declines. Previous investigations have concluded with inconsistent results, based on varying methods employed, seasons and environments, and the scale of the experiments. This study characterized the landscape where seed treatments were common, in terms of floral resources available to bees, sources of contamination. A radius of 2 miles (3.2 km) around an apiary was surveyed for 2 seasons to determine the land use by crop, and to quantify the proportion planted with treated seeds, and what other products were applied during the cropping season, and which of these compounds were found in bee hives. Our survey found that approximately 81% of the landscape was under cultivation, of which 70% was planted with neonicotinoid treated seeds. However, no neonicotinoids were detected in samples of bee hive products. Because pollen could be sampled directly from foraging bees at discrete intervals, and traced back to plant origin, it was used as a bioindicator to determine when neonicotinoids might be present in crops or wild plants. Bees collected relatively little pollen from crops except for a brief period of hot, dry weather. Neonicotinoids were detected infrequently and at low levels, and not at all when bees were visiting crop plants. To test the effects of neonicotinoid ingestion on individual bees in situ, a method was devised to continuously monitor the activities of individual honey bees fed with a sublethal concentration of imidacloprid. Bees that consumed 20 ppb imidacloprid did not suffer acute mortality, but actually appeared to survive 1.7 times as long as untreated bees. This work suggests that neonicotinoids, when properly utilized, may not necessarily pose a greater risk to honey bees than other agricultural chemicals, provided colonies have access to sufficient alternative nutritional sources in the surrounding landscape

Mapping neural responses onto innate and acquired behavior: from insect olfaction to realizing a bio-hybrid chemical recognition system

In many organisms, the sense of smell, driven by the olfactory system, serves as the primary sensory modality that guides a plethora of behaviors such as foraging for food, finding mates, and evading predators. Using an array of biological sensors, the olfactory system converts volatile chemical inputs from an organism’s environment into well-patterned neural responses that inform downstream motor neurons to drive appropriate behaviors (e.g., moving towards food or away from danger). For many external cues, the elicited neural responses are often determined by the genetic makeup of the organism, which assigns an innate preference, or valence, for these different stimuli. However, our environment is constantly in flux, and the same stimulus can be encountered in a variety of different contexts, such as following other cues or under different ambient conditions (e.g., humidity). This can modify the neural activation pattern ascribed to the stimulus and potentially alter the corresponding behavioral output. The objective of this dissertation is to understand how neural responses in the early olfactory system of locusts (Schisctocerca americana) are spatiotemporally structured to robustly represent innate valence in different scenarios to drive appropriate behaviors and how they can be altered through learning. To achieve this goal, we used a large panel of chemically diverse odorants and characterized the neural responses they elicited in the antennal lobe (at the level of ensembles of principal or projection neurons) as well as the innate appetitive behavioral response they produced. We found that neural responses generated both during (ON response) and after (OFF response) termination of the odorant contained information regarding its identity and could be used to predict the innate behavioral outcomes. Notably, predictions made using the ON and the OFF responses differed in the sets of neurons they used to generate the predictions, indicating that neural-behavioral transformations could be achieved in multiple ways. Furthermore, both these ON and OFF neural response classifiers outperformed attempts to predict behavior using chemical features of the stimuli (detected by NMR or IR spectra), indicating that the antennal lobe was transforming and encoding olfactory inputs to map them onto the innate valence associated with the sensory cue. We found that the organization of odor-evoked neural responses that readily map onto innate preferences may also constrain learned odor-reward associations. While odorants with an innate positive behavioral preference alone could support learning odor-reward associations, the conditioned responses were not odor-specific but appeared to generalize to other odorants that evoked similar neural responses. The timing of the behavioral responses could be varied by delivering rewards during epochs when the odorant would generate either the ON or the OFF neural responses. Overall, we found that the organization of ON and OFF neural responses in the antennal lobe clustered into manifolds or subspaces that could be explained using innate behavioral preferences and suitability for reinforcement learning. To understand the robustness of these results, we developed novel minimally invasive experimental methods to record locust neural responses while they actively sampled their surroundings. We found neural responses in this more naturalistic scenario to maintain their manifold organization, and classical conditioning enhanced the separation between neural responses evoked by innately appetitive and non-appetitive odorants. Our results also indicate that neural and behavioral responses in freely moving locusts were consistent with those observed earlier in highly compromised preparations. Finally, we exploited our newly-developed recording techniques to engineer an insect-based chemical sensor that could be used for a real-world application