Sensor technologies have sufficiently advanced to provide low-cost devices that can quantify carbon dioxide levels in honeybee hives with high temporal resolution and in a small enough package for hive deployment. Recent publications have shown that summer carbon dioxide levels vary throughout the day and night over ranges that typically exceed 5000 ppm. Such dramatic changes in a measurable parameter associated with bee physiology are likely to convey information about the colony health. In this work, we present data from four UK-based hives collected through the winter of 2022/2023, with a focus on seeing if carbon dioxide can indicate when colonies are at risk of failure. These hives have been fitted with two Sensirion SCD41 photoacoustic non-dispersive infrared (NDIR) carbon dioxide sensors, one in the queen excluder, at the top of the brood box, and one in the crown board, at the top of the hive. Hive scales have been used to monitor the hive mass, and internal and external temperature sensors have been included. Embedded accelerometers in the central frame of the brood box have been used to measure vibrations. Data showed that the high daily variation in carbon dioxide continued throughout the coldest days of winter, and the vibrational data suggested that daily fanning may be responsible for restoring lower carbon dioxide levels. The process of fanning will draw in colder air to the hive at a time when the bees should be using their energy to maintain the colony temperature. Monitoring carbon dioxide may provide feedback, prompting human intervention when the colony is close to collapse, and a better understanding may contribute to discussions on future hive design.

American foulbrood (AFB) is a serious infectious disease of honeybees (<i>Apis mellifera</i>) caused by <i>Paenibacillus larvae</i>. Increased <i>P. larvae</i> count in hive-related material is associated with an increased risk of AFB. Here, we quantified <i>P. larvae</i> cells in 106 adult bee and 97 hive debris samples using quantitative PCR (qPCR); 66/106 adult bee and 66/97 hive debris samples were collected simultaneously from the same bee colony (paired-sample design). The corresponding bee colonies were also examined for the presence of AFB clinical signs. A binary logistic regression model to distinguish between AFB-affected and unaffected honeybee colonies showed a strong diagnostic accuracy of both sample types for predicting the onset of AFB based on <i>P. larvae</i> counts determined by qPCR. The colonies with a <i>P. larvae</i> count greater than 4.5 log cells/adult bee or 7.3 log cells/mL hive debris had a 50% probability of being clinically affected and were categorized as high-risk. The AFB-unaffected colonies had significantly lower <i>P. larvae</i> counts than the AFB-affected colonies, but the latter did not differ significantly in <i>P. larvae</i> counts in relation to the severity of clinical signs. Both bee-related sample types had a high diagnostic value for predicting disease outcome based on <i>P. larvae</i> counts. These results improve the understanding of the relationship between <i>P. larvae</i> counts and AFB occurrence, which is essential for early detection of high-risk colonies.

The benefits of stingless bee honey for health found in various studies increase the demand for these apiculture activities to grow. However, the production of stingless bee honey requires specific garden design considerations according to the species’ nature and behaviour. Therefore, this study aims to analyse the design of a stingless bee garden by three stingless bee honey entrepreneurs in Kelantan, Malaysia for urban farming consideration. Three stingless bee gardens were selected based on their establishment in producing stingless bee honey for business, namely, (i) RTF Kelulut Garden, (ii) Meloris Kelulut Garden, and (iii) Husna Kelulut Garden. This research employed the mapping method, image capture to collect information on spatial arrangement, hive design, and plant identification for the commercial stingless bee garden. Data were analyzed using comparative analysis to define the significant considerations as well as the recommendation for better garden design. The finding suggests that there are three design layouts for the stingless bee garden which are a covered setting, a natural setting, and a mixed setting. Mix setting becomes the recommended setting suitable in tropical countries because of rainy and hot seasons. This study implies that an understanding of stingless bee garden design assists entrepreneurs in increasing the productivity and quality of stingless bee honey.

Abstract Background Halyomorpha halys Stål (Hemiptera: Pentatomidae), brown marmorated stink bug (BMSB), is a highly polyphagous invasive pest worldwide. It is also known to be a nuisance pest as it enters artificial structures, including human habitats, to overwinter and releases very unpleasant odours when disturbed. Overwintering populations can be trapped and killed collectively by targeting the aggregation behaviour of BMSB adults. However, efficient traps for catching overwinter population have not been yet developed and validated. A novel and effective trapping method would be to design shelter traps in the field that entice and mass-trap overwintering BMSB as they initiate to display their typical aggregation behavior and seek shelter in the traps. Methods In this study conducted in Northwestern China, we designed different BMSB overwintering shelter traps made of different materials (i.e., wood or corflute) and lock types (with/without lock, pyrometric or strip door lock) and tested their efficacy at two different sites and three different locations within sites. We also tested the efficacy of the traps with or without the presence of the BMSB aggregation pheromone. Results Although trapped BMSB numbers were generally low across all traps tested, the black corflute trap was found to attract the highest average number of BMSB males and females, followed by the wooden-made trap, the bee-hive box and finally the wooden-made locked trap, which attracted the lowest numbers of BMSB. The trapping efficacy was found to not be affected by experimental sites or locations nor by the presence of the BMSB aggregation pheromone lure. Conclusions Our results showed that traps made of black corflute with slit doors were generally preferred by overwintering BMSB. This preliminary proof-of-concept study provides valuable information for further improvement of novel overwintering traps that could be used to mass trap BMSB overwintering populations.

El presente estudio se enfocó en evaluar el nivel de proteína cruda y digestibilidad in vitro del suplemento proteico PROSISE®, así como su efecto tanto sobre la postura como sobre la población de colmenas comerciales de Apis mellifera. Para determinar el nivel de proteína cruda y digestibilidad del suplemento PROSISE®, se utilizó el método Kjeldahl y digestibilidad por pepsina, respectivamente. Para determinar el efecto del suplemento PROSISE® sobre el consumo, postura y población de las colmenas comerciales, se emplearon 16 colmenas comerciales del apiario “Abejas del Norte” ubicado en el centro poblado Conache del distrito de Laredo, región La Libertad, a una altura de 764 msnm. Las colmenas fueron distribuidas a través de un diseño completo al azar para evaluar el consumo de alimento, postura y población de las colmenas durante el noviembre del 2021. Las colonias recibieron dos tratamientos (Control: sin suplemento proteico, PS: con suplemento proteico PROSISE®). Los resultados mostraron que el suplemento PROSISE® alcanzó 23.0% de proteína cruda y 91.9% de digestibilidad. Asimismo, el suplemento PROSISE® obtuvo consumo total de 476.83 ±184.16 g/colmena y consumo diario de 22.70 ±8.78 g/colmena. El área de cría del tratamiento con PROSISE® (T1: 7010.00 ±951.60) fue significativamente (p=0.0011) mayor que en el control (T0: 4144.00 ±1228.77). La población de abejas del tratamiento con PROSISE® (T1: 25920.00 ±1254.85) fue significativamente (p=0.0022) mayor que en el control (T0: 17010.00 ±3436.54). Por lo tanto, se concluye que el suplemento proteico PROSISE® alcanzó 23% de proteína cruda y 91.9 % de digestibilidad; y que el empleo de suplemento proteico PROSISE® aumentó la postura y población de abejas en colmenas comerciales situadas en cultivo polifloral | The present study was focused on evaluating the crude protein level and in vitro digestibility of the protein supplement PROSISE®, as well as its effect on both the posture and the population of commercial Apis mellifera hives. To determine the crude protein level and digestibility of the PROSISE® supplement, the Kjeldahl method and pepsin digestibility were used, respectively. To determine the effect of the PROSISE® supplement on the consumption, posture, and population of commercial hives, 16 commercial hives were used from the apiary ""Abejas del Norte"" located in the village of Conache, district of Laredo, La Libertad region, at an altitude of 764 meters above sea level. The hives were distributed through a complete randomized design to evaluate food consumption, posture, and population of the hives during November 2021. The colonies received two treatments (Control: no protein supplement, PS: with PROSISE® protein supplement). The results showed that the PROSISE® supplement achieved 23.0% crude protein and 91.9% digestibility. Likewise, the PROSISE® supplement obtained total consumption of 476.83 ±184.16 g/hive and daily consumption of 22.70 ±8.78 g/hive. The brood area of the PROSISE® treatment (T1: 7010.00 ±951.60) was significantly (p=0.0011) larger than that of the control (T0: 4144.00 ±1228.77). The bee population of PROSISE® treatment (T1: 25920.00 ±1254.85) was significantly (p=0.0022) higher than in the control (T0: 17010.00 ±3436.54). Therefore, it is concluded that PROSISE® protein supplement reached 23% crude protein and 91.9 % digestibility; and that the use of PROSISE® protein supplement increased the laying and population of bees in commercial hives located in polyfloral crop. | Tesis

It tells the story of FLONOR, a bee who feels trapped in the monotonous routine of the hive and longs for something more in life than simply meeting the expectations imposed by the bee society. She meets Ramon, a young human who shares her feeling of being trapped in a world of expectations and social pressures. Ramon is developing a design project for his professional exam, facing similar obstacles as FLONOR in his quest for authenticity and originality.Ramón explains his project, which is to design a new sugarcane transport implement, combining industrial design and engineering. They address the problems of current implements, such as structural deformations, loss of load and lack of road safety. Ramón details the design process, from initial research to CAD modeling and finite element analysis.Both characters share their struggles with authenticity and resistance to conformity. After a deep conversation, Ramon wakes up and realizes that he had been talking to himself through the perspective of a bee in a dream. He is inspired by this experience and carries with him the courage to face his professional examination, remembering the phrase "LIFE IS A DREAM, AND DREAMS ARE DREAMS".In summary, the text is about the search for authenticity and resistance to social expectations, represented by FLONOR and Ramon, who find connection and mutual motivation to move forward on their respective paths to self-realization. | Narra la historia de FLONOR, una abeja que se siente atrapada en la rutina monótona de la colmena y anhela algo más en la vida que simplemente cumplir con las expectativas impuestas por la sociedad de abejas. Se encuentra con Ramón, un joven humano que comparte su sentimiento de estar atrapado en un mundo de expectativas y presiones sociales. Ramón está desarrollando un proyecto de diseño para su examen profesional, enfrentando obstáculos similares a los de FLONOR en su búsqueda de autenticidad y originalidad. Ramón explica su proyecto, que consiste en diseñar un nuevo implemento de transporte para caña de azúcar, combinando diseño industrial e ingeniería. Abordan las problemáticas de los implementos actuales, como deformaciones estructurales, pérdida de carga y falta de seguridad vial. Ramón detalla el proceso de diseño, desde la investigación inicial hasta el modelado en software CAD y el análisis de elementos finitos. Ambos personajes comparten sus luchas por la autenticidad y la resistencia a la conformidad. Después de una conversación profunda, Ramón se despierta y se da cuenta de que había estado hablando consigo mismo a través de la perspectiva de una abeja en un sueño. Se siente inspirado por esta experiencia y lleva consigo la valentía para enfrentar su examen profesional, recordando la frase "LA VIDA ES UN SUEÑO, Y LOS SUEÑOS SUEÑO SON". En resumen, el texto trata sobre la búsqueda de la autenticidad y la resistencia a las expectativas sociales, representadas por FLONOR y Ramón, quienes encuentran conexión y motivación mutua para seguir adelante en sus respectivos caminos hacia la realización personal.

Ecological systems are complex. Ecological models are simplified versions of ecological systems. Scientists use ecological models to better understand and manage ecological systems. I applied ecological models in two areas. The first area is the use of commonness optimization to predict species distributions in space. The second area is the use of a honeybee simulation model for risk assessment of honeybees. Humanity needs to know where species are in order to protect and manage them. Because sampling (data collection) of species is limited, knowledge of species distributions is incomplete. As a result, methods are needed to infer species distributions from sparse sampling data. Species distributions can be predicted from sparse sampling data by ecological models using commonness optimization. Commonness optimization works as follows. First, community-level estimates of species richness, the number of species per site, and dissimilarity, the differences in species between pairs of sites, must be estimated. Both estimates can then be combined to estimate the pairwise commonness, the number of species shared between pairs of sites. Pairwise commonness, in turn, can serve as an optimization target for predicting species distributions. Commonness optimization rearranges species occurrences with the goal that the commonness of the predicted community composition matches the a priori estimated pairwise commonness. Information about the prediction workflow for commonness optimization was incomplete. To address this, my co-authors and I created a how-to guide to facilitate the adoption of commonness optimization by other scientists (Chapter Two). The how-to guide covers all the steps from sparsely sampled survey data to predicted species occurrences. To be specific, we predicted bird species distributions for a tropical megacity. Because estimated richness and dissimilarity account for rare species, commonness optimization may be useful for predicting the occurrence of rare species. However, commonness optimization has not been validated for its ability to predict rare species. In addition, commonness optimization has not been compared with other ecological models. To this end, my co-authors and I validated commonness optimization for its ability to predict the occurrence of rare species (Chapter Three). We also compared the predictions of commonness optimization with those of other models. We did this by using six datasets to predict species occurrence. We found that one of the other methods, multi-label random forest, predicted species occurrence better and about 5000 times faster. Rare species were predicted poorly by commonness optimization, but better by, for example, multi-label random forest. However, the predictions made by commonness optimization had smaller richness and dissimilarity errors than the predictions made by the three other methods. Published results of commonness optimization seemed much better than I would have expected based on my own work on commonness optimization. In short, commonness optimization could correctly predict about 50% of land snail occurrences for 1350 species and about 6.7 million sites (Mokany et al., 2011). In Chapter Four, I reviewed the study design of the land snail predictions. I believe that the land snail occurrences used for validation were probably not kept separate from the predictions. This may have led to overly optimistic results in terms of correctly predicted species occurrences. In Chapter Four, I also used a commonness optimization algorithm to replicate the published predictions for synthetic species communities (Mokany et al., 2011). In short, commonness optimization was able to correctly predict approximately 100% of species occurrences for a total number of 30 species and 100 sites. My predictions successfully replicated these results. However, when I increased the total number of species from 30 to 90, the proportion of correctly predicted species occurrences dropped by half, from 100% to only 50%. These results suggest that predictions for large numbers of species and sites may not result in similarly high proportions of correctly predicted occurrences as predictions for small numbers of species and sites. Overall, my co-authors and I identified one potential strength and several weaknesses of commonness optimization. One potential strength is that commonness optimization can predict community composition with small richness and dissimilarity errors (Chapter Three). This may be useful for predictions where there is a strong emphasis on plausible dissimilarity, but little emphasis on correct species identities. An example is the prediction of initial community composition for simulation studies where all species are assumed to be similar. However, we have also found weaknesses in commonness optimization. First, other models predict species occurrences predict species occurrences better and much faster (Chapter Three). Second, geographically rare species are poorly predicted by commonness optimization (Chapter Three). Because such rare species have little effect on the overall commonness, commonness optimization is unlikely to correctly predict the occurrence of rare species. Third, commonness optimization is slow, as predictions for many sites and species can take weeks. Long computation times may make the future development and improvement of commonness optimization difficult. Risk assessment for honeybees is another useful application of ecological models. Honeybees are important pollinators, and contribute to the global food production. However, honeybees are exposed to multiple stressors. A first example is agrochemicals such as the insecticide imidacloprid. A second example is landscape simplification. Landscape simplification leads to temporal gaps in the availability of nectar and pollen around the honeybee hives. A third example is exposure of honeybees to varroa mites, which transmit deformed wing virus. BEEHAVE (Becher et al., 2014) is a model of a honeybee colony. In short, the BEEHAVE model consists of a simulated honeybee colony with a queen, foragers, workers, drones, and larvae. It is designed to capture the colony dynamics inside the hive and foraging behavior outside the hive. However, the effects of insecticides on honeybees were missing from BEEHAVE. My co-authors and I extended BEEHAVE to include chronic (long-term) and acute (short-term) effects of imidacloprid. We exposed the honeybee colony to three stressors. First, imidacloprid originating from flowering oilseed rape. Second, periods when pollen and nectar were not available. Third, varroa mites carrying the deformed wing virus. We found few acute effects of imidacloprid because the thresholds for such effects were rarely, if ever, reached. However, we found that colony performance was affected by chronic effects. Colony performance was more affected in the second and third years of exposure than in the first year. Our study was the first extension of the BEEHAVE model (Becher et al., 2014) to include the effects of imidacloprid. First, it demonstrates the importance of long-term studies of pesticide effects. Second, it represents a successful application of ecological modeling in the field of risk assessment and food safety. | 2024-07-16